Assignment 2

Due: Sunday, February 9

For this assignment you will experiment with various classification models using subsets of some real-world data sets. In particular, you will use the K-Nearest-Neighbor algorithm to classify text documents, experiment with and compare classifiers that are part of the scikit-learn machine learning package for Python, and use some additional preprocessing capabilities of pandas and scikit-learn packages.

1. K-Nearest-Neighbor (KNN) classification on Newsgroups [Dataset: newsgroups.zip]

For this problem you will use a subset of the 20 Newsgroup data set. The full data set contains 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups and has been often used for experiments in text applications of machine learning techniques, such as text classification and text clustering (see the description of the full dataset). The assignment data set contains a subset of 1000 documents and a vocabulary of 5,500 terms. Each document belongs to one of two classes Hockey (class label 1) and Microsoft Windows (class label 0). The data has already been split (80%, 20%) into training and test data. The class labels for the training and test data are also provided in separate files. The training and test data are on term x document format, containing a row for each term in the vocabulary and a column for each document. The values in the table represent raw term occurence counts. The data has already been preprocessed to extract tokens, remove stop words and perform stemming (so, the terms in the vocabulary are stems not full terms). Please be sure to read the readme.txt file in the distribution.

Your tasks in this problem are the following [Note: for this problem you should not use scikit-learn for classification, but create your own KNN classifer. You may use Pandas, NumPy, standard Python libraries, and Matplotlib.]

The dataset is a subset of the 20 newsgroup corpus http://qwone.com/~jason/20Newsgroups/  in term-document format. This subset has been taken from http://mlg.ucd.ie/content/view/22/ (this data was modified to remove terms that did not appear in any of the documents). Each document belong to one of the two classes {Windows, Hockey}. The original data has been divided into test and train (20%, 80%) subsets.

The files contained in the archive file are as follows: 
In [1]:
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
  1. trainMatrixModified.txt: the term-document frequency matrix for the training documents. Each row of this matrix corresponds to one the terms and each column corresponds to one the documents and the (i,j)th element of the matrix shows the frequency of the ith term in the jth document. This matrix contains 5500 rows and 800 columns.
In [2]:
#term-document frequency matrix
trainMatrixModified = pd.read_csv("trainMatrixModified.txt", header=None , delimiter='\t')
print(trainMatrixModified.shape)
train=trainMatrixModified.T
train
(5500, 800)
Out[2]:
0 1 2 3 4 5 6 7 8 9 ... 5490 5491 5492 5493 5494 5495 5496 5497 5498 5499
0 2.0 2.0 2.0 1.0 8.0 6.0 2.0 8.0 2.0 4.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 1.0 0.0 0.0 0.0 2.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 2.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 2.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
795 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
796 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
797 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
798 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
799 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

800 rows × 5500 columns

  1. modifiedterms.txt: This file contains the set of 5500 terms in the vocabulary. Each line contains a term and corresponds to the corresponding rows in term-document frequency matrices.
In [3]:
#term
modifiedterms = pd.read_csv("modifiedterms.txt", header=None)
print(modifiedterms.shape)
modifiedterms.head()
(5500, 1)
Out[3]:
0
0 david
1 rex
2 wood
3 subject
4 call
  1. trainClasses.txt: This file contains the labels associated with each training document. Each line is in the format of documentIndex \t classId where the documentIndex is in the range of [0,800) and refers to the index of the document in the term-document frequency matrix for train documents. The classId refers to one of the two classes and takes one of the values 0 (for Windows) or 1 (for Hockey).
In [4]:
# Label:  0 (for Windows) or 1 (for Hockey).
trainClasses = pd.read_csv("trainClasses.txt", header=None, delimiter='\t').iloc[:,1:]
print(trainClasses.shape)
trainClasses.columns= range(1)
trainClasses.head()
trainlabel=trainClasses.T
trainlabel
(800, 1)
Out[4]:
0 1 2 3 4 5 6 7 8 9 ... 790 791 792 793 794 795 796 797 798 799
0 0 1 0 1 0 1 1 1 1 1 ... 0 1 1 0 1 0 1 1 1 1

1 rows × 800 columns

  1. testMatrixModified.txt: the term-document frequency for the test documents. The matrix contains 5500 rows and 200 columns.
In [5]:
#term
testMatrixModified = pd.read_csv("testMatrixModified.txt", header=None,delimiter='\t')
print(testMatrixModified.shape)
test=testMatrixModified.T
test
(5500, 200)
Out[5]:
0 1 2 3 4 5 6 7 8 9 ... 5490 5491 5492 5493 5494 5495 5496 5497 5498 5499
0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
195 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
196 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
197 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
198 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
199 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

200 rows × 5500 columns

  1. testClasses.txt: This file contains the labels associated with each test document. Each line is in the format of documentIndex \t classId where the documentIndex is in the range of [0,200) and refers to the index of the document in the term-document frequency matrix for test documents
In [6]:
testClasses=pd.read_csv("testClasses.txt", header=None , delimiter='\t').iloc[:,1:]
print(testClasses.shape)
testClasses.columns= range(1)
testlabel=testClasses.T
testlabel
(200, 1)
Out[6]:
0 1 2 3 4 5 6 7 8 9 ... 190 191 192 193 194 195 196 197 198 199
0 1 0 0 1 1 0 1 1 0 1 ... 0 0 0 0 0 1 1 0 1 1

1 rows × 200 columns

In [7]:
termFreqs = trainMatrixModified.sum(axis=1)
print(sorted(termFreqs,reverse=True)[:10])
plt.plot(sorted(termFreqs, reverse=True))
plt.show()
#zipf distribution
[959.0, 720.0, 680.0, 578.0, 545.0, 483.0, 470.0, 429.0, 401.0, 378.0]
In [8]:
test=np.array(test)
train=np.array(train)
test[0].shape
Out[8]:
(5500,)

a.

Create your own KNN classifier function. Your classifier should allow as input the training data matrix, the training labels, the instance to be classified, the value of K, and should return the predicted class for the instance and the indices of the top K neighbors. Your classifier should work with Euclidean distance as well as Cosine Similarity. You may create two separate classifiers, or add the distance metric as a parameter in the classifier function.

Create KNN classifier function

In [9]:
from collections import Counter

def knn_search(x, D, K, measure):
    """ find K nearest neighbors of an instance x among the instances in D """
    if measure == 0:
        # euclidean distances from the other points
        dists = np.sqrt(((D - x)**2).sum(axis=1))
    elif measure == 1:
        # first find the vector norm for each instance in D as wel as the norm for vector x
        D_norm = np.array([np.linalg.norm(D[i]) for i in range(len(D))])
        x_norm = np.linalg.norm(x)
        # Compute Cosine: divide the dot product o x and each instance in D by the product of the two norms
        sims = np.dot(D,x)/(D_norm * x_norm)
        # The distance measure will be the inverse of Cosine similarity
        dists = 1-sims
    idx = np.argsort(dists) # sorting
    # return the indexes of K nearest neighbors
    return idx[:K], dists

neigh_idx, distances = knn_search(test[1], train, 5, 0)
print("euclidean distances: \n \n{} \n".format(neigh_idx))

neigh_idx, distances = knn_search(test[1], train, 5, 1)
print("Cosine similarity: \n \n{} \n".format(neigh_idx))
euclidean distances: 
 
[798 554 757 224  38] 

Cosine similarity: 
 
[382 775 163 587 550] 

from sklearn import neighbors

def findbest_knn(k,weight): order=0 best=0 for i in range(1,k):
n_neighbors = i knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights=weight) knnclf.fit(train, trainClasses) knnpreds_test = knnclf.predict(test)

    #print(np.ravel(knnpreds_test))
    knncm = confusion_matrix(list(np.ravel(testClasses)), knnpreds_test)
    #print(i)
    #print(knncm)
    accuracy=knnclf.score(test, testClasses)
    #print(accuracy)
    if accuracy > best:
    #    print(knncm)
        order=i
        #print(i)
        best=accuracy
        #print(best)
return knncm,order,best


findbest_knn(10,'distance')

b.

Create an evaluation function to measure the effectiveness of your classifier. This function will call the classifier function in part a on all the test instances and in each case compares the actual test class label to the predicted class label. It should take as input the training data, the training labels, the test instances, the labels for test instances, and the value of K. Your evaluation function should return three the Classification Accuracy (ratio of correct predictions to the number of test instances) [See class notes: Classification & Prediction - Review of Basic Concepts].

Create an evaluation function to measure the effectiveness of your classifier

In [10]:
from sklearn import neighbors
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

def evaluation(k,method):
    errorCount = 0.0
    test_perdict=[]
    rows=testMatrixModified.shape[1]
    for i in range(rows):
        neigh_idx, distances = knn_search(test[i], train, k , method)
#distances = pd.Series(distances, index=train.index)
#distances
#print("Query:", testMatrixModified[1])
#print("\nNeighbors:")
#print(neigh_idx)
        neigh_labels = trainClasses.iloc[neigh_idx][0]
#from collections import Counter
        perdict=Counter(neigh_labels).most_common(1)
        test_perdict.append(perdict[0][0])
        #print(perdict[0][0])
        #print(testlabel[i])
        if (perdict[0][0] != testlabel[i][0]): 
            #print(str(perdict[0][0]))
            #print(str(testlabel[i][0]))
            errorCount += 1.0
    accuracy=1-errorCount/float(rows)
    #print("the total accuracy rate is: ", (1-errorCount/float(rows)))
    knncm=confusion_matrix(testClasses, test_perdict)
    #print(knncm)
    return accuracy, knncm                          

#main(0)
accuracy, knncm =evaluation(9,1)
#print("the total accuracy rate is: ", accuracy)
print("Accuracy: {}".format(accuracy))
print("Confusion Matrix: \n{}".format(knncm))
Accuracy: 0.975
Confusion Matrix: 
[[98  1]
 [ 4 97]]

c.

Run your evaluation function on a range of values for K in order to compare accuracy values for different numbers of neighbors. Do this both using Euclidean Distance as well as Cosine similarity measure. [For example, you can try evaluating your classifiers on a range of values of K from 1 through 20 (or greater)]. Present the results as graphs with K in the x-axis and the evaluation metric (accuracy) on the y-axis.

Use a single plot to compare the two version of the classifier (Eculidean distanve version vs. cosine similarity version).

In [11]:
k=[]
ED_Accuracy=[]
CS_Accuracy=[]
for i in  range(1,21):
    #print(i)
    k.append(i)
    csaccuracy, knncm = evaluation(i,1)
    CS_Accuracy.append(csaccuracy)
    #print(i)
    edaccuracy,knncm = evaluation(i,0)
    ED_Accuracy.append(edaccuracy)
    
print(k)
print(ED_Accuracy)
print(CS_Accuracy)

plt.plot(np.array(k), ED_Accuracy, label='ED_Accuracy')
plt.plot(k, CS_Accuracy, label='CS_Accuracy')
plt.legend( ('ED_Accuracy','CS_Accuracy') )
plt.ylabel('Accuracy')
plt.xlabel('k')
plt.show()
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
[0.78, 0.78, 0.81, 0.81, 0.815, 0.835, 0.765, 0.8, 0.75, 0.85, 0.795, 0.845, 0.775, 0.815, 0.785, 0.8, 0.76, 0.785, 0.74, 0.765]
[0.985, 0.985, 0.97, 0.985, 0.97, 0.985, 0.98, 0.98, 0.975, 0.985, 0.98, 0.975, 0.98, 0.98, 0.985, 0.98, 0.975, 0.975, 0.975, 0.975]

d.

Next, modify the training and test data sets so that term weights are converted to TFxIDF weights (instead of raw term frequencies). [See class notes on Text Categorization]. Then, rerun your evaluation (only for the Cosine similairty version of the classifier) on the range of K values (as above) and compare the results to the results without using TFxIDF weights.

TFxIDF weights

Train dataset
In [12]:
numTerms=train.shape[0]
#print(numTerms)
#800
NDocs = train.shape[1]
#print(NDocs)
#5500

# data frequency,sum up all the same rows
DF = pd.DataFrame([(train!=0).sum(1)]).T
#print(DF)

#creat full 5500 document martix
NMatrix=np.ones(np.shape(train), dtype=float)*NDocs
np.set_printoptions(precision=2,suppress=True,linewidth=100)
#print(NMatrix)

# Convert each entry into IDF values
# IDF is the log of the inverse of document frequency
# Note that IDF is only a function of the term, so all columns will be identical.
IDF = np.log2(np.divide(NMatrix, np.array(DF)))
#print(IDF)
TD_tfidf = train * IDF
pd.set_option("display.precision", 2)
pd.DataFrame(TD_tfidf)
Out[12]:
0 1 2 3 4 5 6 7 8 9 ... 5490 5491 5492 5493 5494 5495 5496 5497 5498 5499
0 11.62 11.62 11.62 5.81 46.48 34.86 11.62 46.48 11.62 23.24 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.00 0.00 0.00 6.49 6.49 0.00 0.00 0.00 0.00 0.00 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.00 0.00 0.00 7.00 0.00 0.00 0.00 14.00 0.00 7.00 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 14.59 0.00 0.00 7.30 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 15.45 0.00 0.00 7.72 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
795 0.00 0.00 0.00 7.84 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
796 0.00 0.00 0.00 7.38 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
797 0.00 0.00 0.00 6.22 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
798 0.00 0.00 0.00 11.43 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
799 0.00 0.00 0.00 6.09 6.09 0.00 0.00 0.00 0.00 0.00 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

800 rows × 5500 columns

Test data set
In [13]:
numTerms=test.shape[0]
#print(numTerms)
#800
NDocs = test.shape[1]
#print(NDocs)
#5500

# data frequency,sum up all the same rows
DF = pd.DataFrame([(test!=0).sum(1)]).T
#print(DF)

#creat full 5500 document martix
NMatrix=np.ones(np.shape(test), dtype=float)*NDocs
np.set_printoptions(precision=2,suppress=True,linewidth=100)
#print(NMatrix)

# Convert each entry into IDF values
# IDF is the log of the inverse of document frequency
# Note that IDF is only a function of the term, so all columns will be identical.
IDF = np.log2(np.divide(NMatrix, np.array(DF)))
#print(IDF.shape)
Test_tfidf = test * IDF
pd.set_option("display.precision", 2)
pd.DataFrame(Test_tfidf)
Out[13]:
0 1 2 3 4 5 6 7 8 9 ... 5490 5491 5492 5493 5494 5495 5496 5497 5498 5499
0 0.00 0.0 0.0 5.55 0.0 0.0 0.0 0.0 0.0 5.55 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.00 0.0 0.0 6.87 0.0 0.0 0.0 0.0 0.0 0.00 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 6.78 0.0 0.0 6.78 0.0 0.0 0.0 0.0 0.0 0.00 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.00 0.0 0.0 4.50 0.0 0.0 0.0 0.0 0.0 0.00 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 0.00 0.0 0.0 6.67 0.0 0.0 0.0 0.0 0.0 6.67 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
195 0.00 0.0 0.0 7.38 0.0 0.0 0.0 0.0 0.0 7.38 ... 7.38 7.38 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
196 0.00 0.0 0.0 5.01 0.0 0.0 0.0 0.0 0.0 0.00 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
197 0.00 0.0 0.0 9.26 0.0 0.0 0.0 0.0 0.0 0.00 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
198 0.00 0.0 0.0 6.54 0.0 0.0 0.0 0.0 0.0 6.54 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
199 0.00 0.0 0.0 7.97 0.0 0.0 0.0 0.0 0.0 0.00 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

200 rows × 5500 columns

In [14]:
DT_tfidf = TD_tfidf
#print(DT_tfidf)
DT_array = np.array(DT_tfidf)
#print(DT_array)
#neigh_idx, distances = knn_search(test_tfidf[1], DT_array, 5, 1)
#for i in range(rows):
#    test_tfidf = testMatrixModified[i]* IDF[0]
#    neigh_idx, distances = knn_search(test_tfidf.T, DT_array, 10, 1)
#   # print(neigh_idx)

def evaluation_tfidf(k,method):
    errorCount = 0.0
    test_perdict=[]
    rows=testlabel.shape[1]
    #print(rows)
    for i in range(rows):
        test_tfidf = Test_tfidf[i]
        neigh_idx, distances = knn_search(test_tfidf.T, DT_array, k, method)
#distances = pd.Series(distances, index=train.index)
#distances
#print("Query:", testMatrixModified[1])
#print("\nNeighbors:")
#print(neigh_idx)
        neigh_labels = trainClasses.iloc[neigh_idx][0]
#from collections import Counter
        perdict=Counter(neigh_labels).most_common(1)
        test_perdict.append(perdict[0][0])
        #print(perdict[0][0])
        #print(testlabel[i][0])
        if (perdict[0][0] != testlabel[i][0]): 
            #print(str(perdict[0][0]))
            #print(str(testlabel[i][0]))
            errorCount += 1.0
    accuracy=1-errorCount/float(rows)
    #print("the total accuracy rate is: ", (1-errorCount/float(rows)))
    knncm=confusion_matrix(testClasses, test_perdict)
    #print(knncm)
    return accuracy, knncm                          

#main(0)
for i in range(1,10):
    accuracy, knncm =evaluation_tfidf(i,1)
    print("the total accuracy rate is: ", accuracy)
    print(knncm)
the total accuracy rate is:  0.985
[[98  1]
 [ 2 99]]
the total accuracy rate is:  0.985
[[98  1]
 [ 2 99]]
the total accuracy rate is:  0.97
[[98  1]
 [ 5 96]]
the total accuracy rate is:  0.99
[[ 98   1]
 [  1 100]]
the total accuracy rate is:  0.97
[[97  2]
 [ 4 97]]
the total accuracy rate is:  0.985
[[99  0]
 [ 3 98]]
the total accuracy rate is:  0.98
[[98  1]
 [ 3 98]]
the total accuracy rate is:  0.98
[[98  1]
 [ 3 98]]
the total accuracy rate is:  0.975
[[98  1]
 [ 4 97]]

In this range of accuracy, when k is 4 has the most best accuracy rate, 0.99.

e.

Create a new classifier based on the Rocchio Method adapted for text categorization [See class notes on Text Categorization]. You should separate the training function from the classifiation function. The training part for the classifier can be implemented as a function that takes as input the training data matrix and the training labels, returning the prototype vectors for each class. The classification part can be implemented as another function that would take as input the prototypes returned from the training function and the instance to be classified. This function should measure Cosine similarity of the test instance to each prototype vector. Your output should indicate the predicted class for the test instance and the similarity values of the instance to each of the category prototypes.
Finally, use your evaluation function to compare your results to the best KNN results you obtained earlier. [Note: your functions should work regardless of the number of categories (class labels) and should not be limited to two-class categorization scenario.]

2. Classification using scikit-learn [Dataset: bank_data.csv]

For this problem you will experiment with various classifiers provided as part of the scikit-learn (sklearn) machine learning module, as well as with some of its preprocessing and model evaluation capabilities. [Note: This module is already part of the Anaconda distributions. However, if you are using standalone Python distributions, you will need to first obtain and install it]. You will work with a modified subset of a real data set of customers for a bank. This is the same data set used in Assignment 1. The data is provided in a CSV formatted file with the first row containing the attribute names. The description of the the different fields in the data are provided in this document.

Your tasks in this problem are the following:

In [16]:
BD = pd.read_csv("bank_data.csv", header=0)
#print(BD.columns)
BDmaxtrix=pd.get_dummies(BD[['age', 'income', 'children', 'gender', 'region', 'married', 'car',
       'savings_acct', 'current_acct', 'mortgage']])
print(BDmaxtrix.head(2))
BDpep = BD["pep"]
BDpep.head()
   age   income  children  gender_FEMALE  gender_MALE  region_INNER_CITY  \
0   48  17546.0         1              1            0                  1   
1   40  30085.1         3              0            1                  0   

   region_RURAL  region_SUBURBAN  region_TOWN  married_NO  married_YES  \
0             0                0            0           1            0   
1             0                0            1           0            1   

   car_NO  car_YES  savings_acct_NO  savings_acct_YES  current_acct_NO  \
0       1        0                1                 0                1   
1       0        1                1                 0                0   

   current_acct_YES  mortgage_NO  mortgage_YES  
0                 0            1             0  
1                 1            0             1  
Out[16]:
0    YES
1     NO
2     NO
3     NO
4     NO
Name: pep, dtype: object

a.

Load and preprocess the data using Pandas or similar tools. Specifically, you need to separate the target attribute ("pep") from the portion of the data to be used for training and testing. You will need to convert the selected dataset into the Standard Spreadsheet format (scikit-learn functions generally assume that all attributes are in numeric form). Finally, you need to split the transformed data into training and test sets (using 80%-20% randomized split). [Review Jupyter Notebooks from class to see examples of how to perform these tasks.]

In [17]:
from sklearn.model_selection import train_test_split

#split the transformed data into training and test sets (using 80%-20% randomized split)
BD_train, BD_test, BD_target_train, BD_target_test = train_test_split(BDmaxtrix, BDpep, 
                                                                      test_size=0.2, random_state=33)
print(BD_test.shape)
BD_test[0:5]
(120, 19)
Out[17]:
age income children gender_FEMALE gender_MALE region_INNER_CITY region_RURAL region_SUBURBAN region_TOWN married_NO married_YES car_NO car_YES savings_acct_NO savings_acct_YES current_acct_NO current_acct_YES mortgage_NO mortgage_YES
456 32 13267.6 0 1 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1
366 59 29866.9 1 1 0 1 0 0 0 1 0 0 1 1 0 0 1 1 0
131 61 41609.5 3 0 1 0 1 0 0 0 1 1 0 0 1 0 1 0 1
448 53 48971.6 3 0 1 0 1 0 0 0 1 0 1 0 1 1 0 1 0
337 65 38080.9 1 0 1 1 0 0 0 0 1 0 1 0 1 1 0 0 1
In [18]:
#Performing min-max normalization to rescale numeric attributes

from sklearn import preprocessing

min_max_scaler = preprocessing.MinMaxScaler().fit(BD_train)

BD_train_norm = min_max_scaler.transform(BD_train)
BD_train_norm = pd.DataFrame(BD_train_norm, columns=BD_train.columns, index=BD_train.index)


BD_test_norm = min_max_scaler.transform(BD_test)
BD_test_norm = pd.DataFrame(BD_test_norm, columns=BD_test.columns, index=BD_test.index)

b.

Run scikit-learn's KNN classifier on the test set.

Note: in the case of KNN, you should first normalize the data so that all attributes are in the same scale (normalize so that the values are between 0 and 1).

Generate the confusion matrix (visualize it using Matplotlib), as well as the classification report. Also, compute the average accuracy score.

Experiment with different values of K and the weight parameter (i.e., with or without distance weighting) for KNN to see if you can improve accuracy (you do not need to provide the details of all of your experimentation, but provide a short discussion on what parameters worked best as well as your final results).

In [19]:
from sklearn import neighbors
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
In [20]:
## ‘uniform’ : uniform weights. All points in each neighborhood are weighted equally.
##‘distance’ : weight points by the inverse of their distance. 

def findbest_knn(k,weight):
    order=0
    best=0
    for i in range(1,k):    
        n_neighbors = i
        knnclf = neighbors.KNeighborsClassifier(n_neighbors, weights=weight)
        knnclf.fit(BD_train_norm, BD_target_train)
        knnpreds_test = knnclf.predict(BD_test_norm)
        knncm = confusion_matrix(BD_target_test, knnpreds_test)
        #print(i)
        #print(knncm)
        accuracy=knnclf.score(BD_test_norm, BD_target_test)
        #print(accuracy)
        if accuracy > best:
        #    print(knncm)
            order=i
            #print(i)
            best=accuracy
            #print(best)
    return knncm,order,best

for i in [5,10,25,50]:
    uniform_knncm,order,best=findbest_knn(i,'uniform')
    print("If K is 1 to {} then and weight is uniform, the best k will be {} and accuracy will be {}".format(i,order,best))
    distance_knncm,order,best=findbest_knn(i,'distance')
    print("If K is 1 to {} then and weight is distance, the best k will be {} and accuracy will be {}".format(i,order,best))
    #plt.matshow(knncm)
    #plt.title('Confusion matrix')
    #plt.colorbar()
    #plt.ylabel('Actual')
    #plt.xlabel('Predicted')
    #plt.show()
If K is 1 to 5 then and weight is uniform, the best k will be 3 and accuracy will be 0.6583333333333333
If K is 1 to 5 then and weight is distance, the best k will be 3 and accuracy will be 0.6416666666666667
If K is 1 to 10 then and weight is uniform, the best k will be 8 and accuracy will be 0.6833333333333333
If K is 1 to 10 then and weight is distance, the best k will be 5 and accuracy will be 0.6583333333333333
If K is 1 to 25 then and weight is uniform, the best k will be 17 and accuracy will be 0.7
If K is 1 to 25 then and weight is distance, the best k will be 18 and accuracy will be 0.6833333333333333
If K is 1 to 50 then and weight is uniform, the best k will be 29 and accuracy will be 0.7333333333333333
If K is 1 to 50 then and weight is distance, the best k will be 36 and accuracy will be 0.7166666666666667

With different weights, have different k in good accuracy. Base on above testing, for k in 29 and weight uniform has 73% accuracy andfor k in 36 and weight distance has 72% accuracy

c.

Repeat the classification using scikit-learn's decision tree classifier (using the default parameters) and the Naive Bayes (Gaussian) classifier. As above, generate the confusion matrix, classification report, and average accuracy scores for each classifier. For each model, compare the average accuracry scores on the test and the training data sets. What does the comparison tell you in terms of bias-variance trade-off?

In [21]:
from sklearn import tree

def findbest_tree(k):
    order=0
    best=0
    if k<3:
        print("plz set your min samples split above 3.")
    elif k ==3:
        findbest_tree(4)
    else:
        for i in range(3,k):  
            treeclf = tree.DecisionTreeClassifier(criterion='entropy', min_samples_split=i)
            treeclf = treeclf.fit(BD_train_norm, BD_target_train)
            treepreds_test = treeclf.predict(BD_test_norm)
            accuracy=treeclf.score(BD_test_norm, BD_target_test)
            treecm = confusion_matrix(BD_target_test, treepreds_test)
            #print(i)
            if accuracy > best:
                #print(treecm)
                order=i
                #print(i)
                best=accuracy
                #print(best)
        return treecm,order,best

for i in [5,10,25,50]:
    treecm,order,best=findbest_tree(i)
    print("If K is 1 to {},  the best k will be {} and accuracy will be {}".format(i,order,best))
#print(treepreds_test)
#print(treeclf.score(BD_test_norm, BD_target_test))
#print(treeclf.score(BD_train_norm, BD_target_train))
If K is 1 to 5,  the best k will be 3 and accuracy will be 0.8166666666666667
If K is 1 to 10,  the best k will be 6 and accuracy will be 0.8166666666666667
If K is 1 to 25,  the best k will be 23 and accuracy will be 0.85
If K is 1 to 50,  the best k will be 23 and accuracy will be 0.85

With different min_samples_split, have different min_samples_split in good accuracy. Base on above testing, for min_samples_split in 23 has the most accuracy , and for the bias-variance trade-off, it seems that 23 will be the sweet spot so when min_samples_split lager might overcomplicate have more bias, also when min_samples_split too small might oversimplifiy have more bias

In [22]:
from sklearn import naive_bayes

nbclf = naive_bayes.GaussianNB()
nbclf = nbclf.fit(BD_train_norm, BD_target_train)
nbpreds_test = nbclf.predict(BD_test_norm)
accuracy=nbclf.score(BD_test_norm, BD_target_test)
print(accuracy)
print(nbclf.score(BD_train_norm, BD_target_train))
0.5916666666666667
0.6708333333333333

3. Data Analysis and Predictive Modeling on Census data [Dataset: adult-modified.csv]

For this problem you will use a simplified version of the Adult Census Data Set. In the subset provided here, some of the attributes have been removed and some preprocessing has been performed.

Your tasks in this problem are the following:

In [23]:
AdultMod = pd.read_csv("adult-modified.csv", header=0)
AdultMod=AdultMod.replace({'?': np.nan})
AdultMod=AdultMod.astype({'age': 'float64'})
AdultMod
Out[23]:
age workclass education marital-status race sex hours-per-week income
0 39.0 Public 13 Single White Male 40 <=50K
1 50.0 Self-emp 13 Married White Male 13 <=50K
2 38.0 Private 9 Single White Male 40 <=50K
3 53.0 Private 7 Married Black Male 40 <=50K
4 28.0 Private 13 Married Black Female 40 <=50K
... ... ... ... ... ... ... ... ...
9995 38.0 Private 10 Married White Male 60 >50K
9996 25.0 Private 9 Single White Female 8 <=50K
9997 21.0 Private 10 Single Black Male 40 <=50K
9998 NaN Private 2 Married White Male 53 <=50K
9999 39.0 Private 10 Single White Female 40 <=50K

10000 rows × 8 columns

Preprocessing and data analysis:

Examine the data for missing values. In case of categorical attributes, remove instances with missing values. In the case of numeric attributes, impute and fill-in the missing values using the attribute mean.

In [24]:
AdultMod.describe(include="all")
AdultMod.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 8 columns):
age               9802 non-null float64
workclass         9412 non-null object
education         10000 non-null int64
marital-status    10000 non-null object
race              10000 non-null object
sex               10000 non-null object
hours-per-week    10000 non-null int64
income            10000 non-null object
dtypes: float64(1), int64(2), object(5)
memory usage: 625.1+ KB
In [25]:
print("There are 777 rows have missing value"+str((AdultMod[AdultMod.isnull().any(axis=1)]).shape))
There are 777 rows have missing value(777, 8)
In [26]:
#In the case of numeric attributes, impute and fill-in the missing values using the attribute mean.
age_mean = AdultMod.age.mean()
AdultMod.age.fillna(age_mean, axis=0, inplace=True)
AdultMod
Out[26]:
age workclass education marital-status race sex hours-per-week income
0 39.00 Public 13 Single White Male 40 <=50K
1 50.00 Self-emp 13 Married White Male 13 <=50K
2 38.00 Private 9 Single White Male 40 <=50K
3 53.00 Private 7 Married Black Male 40 <=50K
4 28.00 Private 13 Married Black Female 40 <=50K
... ... ... ... ... ... ... ... ...
9995 38.00 Private 10 Married White Male 60 >50K
9996 25.00 Private 9 Single White Female 8 <=50K
9997 21.00 Private 10 Single Black Male 40 <=50K
9998 38.45 Private 2 Married White Male 53 <=50K
9999 39.00 Private 10 Single White Female 40 <=50K

10000 rows × 8 columns

In [27]:
print("There are 588 rows have missing value"+str((AdultMod[AdultMod.isnull().any(axis=1)].shape)))#588 rows missing
#In case of categorical attributes, remove instances with missing values.
AdultMod.drop(AdultMod[AdultMod.workclass.isnull()].index, axis=0, inplace=True)
There are 588 rows have missing value(588, 8)

Examine the characteristics of the attributes, including relevant statistics for each attribute, histograms illustrating the distribtions of numeric attributes, bar graphs showing value counts for categorical attributes, etc.

In [28]:
fig = plt.figure(figsize=(25,25))
####print("histogram")
ax1 = fig.add_subplot(3,3,1)
AdultMod["age"].plot(kind="hist", bins=10,title='age')

ax2 = fig.add_subplot(3,3,2)
AdultMod["hours-per-week"].plot(kind="hist", bins=10,title="hours-per-week")

ax3 = fig.add_subplot(3,3,3)
AdultMod["education"].plot(kind="hist", bins=10,title='education')

print("bar chat")
ax4 = fig.add_subplot(3,3,4)
AdultMod["workclass"].value_counts().plot(kind='bar',title="workclass")

ax5 = fig.add_subplot(3,3,5)
AdultMod["marital-status"].value_counts().plot(kind='bar',title ="marital-status")

ax6 = fig.add_subplot(3,3,6)
AdultMod["race"].value_counts().plot(kind='bar',title ="race")

ax7 = fig.add_subplot(3,3,7)
AdultMod["sex"].value_counts().plot(kind='bar',title ="sex")

ax8 = fig.add_subplot(3,3,8)
AdultMod["income"].value_counts().plot(kind='bar',title ="income")
bar chat
Out[28]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a28ecabe0>

for the age, hours per week and educaiton, we discover that the distrubution is not rnomal, so maybe we can normalize or re collect the data. also for the other attributes, only the marital-status are almost equal, others have unfair counts.

Perform the following cross-tabulations (including generating bar charts): education+race, work-class+income, work-class+race, and race+income. In the latter case (race+income) also create a table or chart showing percentages of each race category that fall in the low-income group. Discuss your observations from this analysis.

In [29]:
fig = plt.figure(figsize=(20,16))

er=pd.crosstab(AdultMod.education,AdultMod.race)
print(er)
er.plot.bar(stacked=True)
plt.legend(title='race')
race       Amer-Indian  Asian  Black  Hispanic  White
education                                            
1                    0      0      1         0     11
2                    0      1      4         3     38
3                    0      4      5         1     71
4                    5      5     14         6    150
5                    0      3     19         2    118
6                    8      3     30         4    223
7                    4      6     49         4    261
8                    0      2     17         3     78
9                   35     67    350        23   2590
10                  26     64    206        11   1818
11                   5     10     33         4    337
12                   4      5     33         3    259
13                   5     75    102         8   1387
14                   0     27     20         1    467
15                   0     11      5         2    153
16                   0      8      4         0    101
Out[29]:
<matplotlib.legend.Legend at 0x11838e198>
<Figure size 1440x1152 with 0 Axes>

In this garph most proportion are in 9,10 ,13 and also race in White has the most in each of them!

In [30]:
wi=pd.crosstab(AdultMod["workclass"],AdultMod["income"])
print(wi)
wi.plot.bar(stacked=True)
plt.legend(title='income')
income     <=50K  >50K
workclass             
Private     5443  1504
Public       925   392
Self-emp     725   423
Out[30]:
<matplotlib.legend.Legend at 0x1185285c0>

In this garph, public and Self-emp is around 3 times or 2 times difference in income, but Private workclass has the huge gap almost close to 5 times!

In [31]:
wr=pd.crosstab(AdultMod["workclass"],AdultMod["race"])
print(wr)
wr.plot.bar(stacked=True)
plt.legend(title='race')
race       Amer-Indian  Asian  Black  Hispanic  White
workclass                                            
Private             65    204    664        64   5950
Public              20     48    192         5   1052
Self-emp             7     39     36         6   1060
Out[31]:
<matplotlib.legend.Legend at 0x11839e630>

In this garph, seems Private and Public has similar patten but Self-emp Asian and Black are close same to Hispanic and Amer-Indian. White has more percent people are Self-emp than othger race.

In [32]:
ri=pd.crosstab(AdultMod["race"],AdultMod["income"]).apply(lambda r: r/r.sum(), axis=1)
print(ri)
ri.plot.bar(stacked=True)
plt.legend(title='income')
income       <=50K  >50K
race                    
Amer-Indian   0.90  0.10
Asian         0.77  0.23
Black         0.87  0.13
Hispanic      0.92  0.08
White         0.74  0.26
Out[32]:
<matplotlib.legend.Legend at 0x1183938d0>
In this we discover that Whtie and Asian has more percentage people has over 50k in their race!

Compare and contrast the characteristics of the low-income and high-income categories across the different attributes.

Predictive Modeling and Model Evaluation:

Using either Pandas or Scikit-learn, create dummy variables for the categorical attributes. Then separate the target attribute ("income>50K") from the attributes used for training. [Note: you need to drop "income<=50K" which is also created as a dummy variable in earlier steps).

In [33]:
dum_AdultMod=pd.get_dummies(AdultMod)
dum_AdultMod.drop(['income_<=50K'], axis=1,inplace=True)
#print(dum_AdultMod.columns)
data_all=dum_AdultMod[['age', 'education', 'hours-per-week', 'workclass_Private',
       'workclass_Public', 'workclass_Self-emp', 'marital-status_Married',
       'marital-status_Single', 'race_Amer-Indian', 'race_Asian', 'race_Black',
       'race_Hispanic', 'race_White', 'sex_Female', 'sex_Male']]
data_class=dum_AdultMod['income_>50K']
#train_class

AM_train, AM_test, AM_target_train, AM_target_test = train_test_split(data_all, data_class, 
                                                                      test_size=0.2, random_state=33)

print(AM_test.shape)
AM_test[0:5]
(1883, 15)
Out[33]:
age education hours-per-week workclass_Private workclass_Public workclass_Self-emp marital-status_Married marital-status_Single race_Amer-Indian race_Asian race_Black race_Hispanic race_White sex_Female sex_Male
8419 56.0 10 45 1 0 0 1 0 0 0 0 0 1 0 1
2629 54.0 9 45 1 0 0 1 0 0 0 0 0 1 0 1
2523 52.0 11 35 1 0 0 0 1 0 0 0 0 1 1 0
7143 42.0 16 60 0 0 1 1 0 0 0 0 0 1 0 1
5902 30.0 9 50 0 0 1 1 0 0 0 0 0 1 0 1

Use scikit-learn to build classifiers uisng Naive Bayes (Gaussian), decision tree (using "entropy" as selection criteria), and linear discriminant analysis (LDA). For each of these perform 10-fold cross-validation (using cross-validation module in scikit-learn) and report the overall average accuracy.

In [34]:
from sklearn.model_selection import cross_val_score
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

nbclf = naive_bayes.GaussianNB()
nb_scores = cross_val_score(nbclf, data_all, data_class, cv=10)
#print("Naive Bayes (Gaussian) \n{}".format(cv_scores))
print("Naive Bayes (Gaussian) \n Overall Accuracy: %0.2f (+/- %0.2f)" % 
      (nb_scores.mean(), nb_scores.std() * 2))

treeclf = tree.DecisionTreeClassifier(criterion='entropy', min_samples_split=5)
DT_scores = cross_val_score(treeclf, data_all, data_class, cv=10)
#print("Decision Tree \n{}".format(DT_scores))
print("Decision Tree \n Overall Accuracy: %0.2f (+/- %0.2f)" % 
      (DT_scores.mean(), DT_scores.std() * 2))

ldclf = LinearDiscriminantAnalysis()
LD_scores = cross_val_score(ldclf, data_all, data_class, cv=10)
#print("Linear Discriminant Analysis \n{}".format(LD_scores))
print("Linear Discriminant Analysis \n Overall Accuracy: %0.2f (+/- %0.2f)" % 
      (LD_scores.mean(), LD_scores.std() * 2))
Naive Bayes (Gaussian) 
 Overall Accuracy: 0.72 (+/- 0.02)
Decision Tree 
 Overall Accuracy: 0.77 (+/- 0.02)
/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
Linear Discriminant Analysis 
 Overall Accuracy: 0.81 (+/- 0.02)

For the decision tree model (generated on the full training data), generate a visualization of tree and submit it as a separate file (png, jpg, or pdf) or embed it in the Jupyter Notebook.

In [35]:
import graphviz
from sklearn.tree import export_graphviz

treeclf = tree.DecisionTreeClassifier(criterion='entropy', min_samples_split=5)
treeclf = treeclf.fit(data_all, data_class)
print ("Accuracy on Training: ",  treeclf.score(data_all, data_class))

export_graphviz(treeclf,out_file='tree.dot', feature_names=data_all.columns, class_names=['0','1'])

with open("tree.dot") as f:
    dot_graph = f.read()
graphviz.Source(dot_graph, format="pdf")
Accuracy on Training:  0.912027199320017
Out[35]:
Tree 0 marital-status_Married <= 0.5 entropy = 0.806 samples = 9412 value = [7093, 2319] class = 0 1 education <= 12.5 entropy = 0.354 samples = 4675 value = [4363, 312] class = 0 0->1 True 904 education <= 11.5 entropy = 0.983 samples = 4737 value = [2730, 2007] class = 0 0->904 False 2 age <= 31.5 entropy = 0.189 samples = 3675 value = [3569, 106] class = 0 1->2 453 age <= 29.5 entropy = 0.734 samples = 1000 value = [794, 206] class = 0 1->453 3 hours-per-week <= 40.5 entropy = 0.067 samples = 1994 value = [1978, 16] class = 0 2->3 102 hours-per-week <= 42.5 entropy = 0.301 samples = 1681 value = [1591, 90] class = 0 2->102 4 age <= 21.5 entropy = 0.039 samples = 1675 value = [1668, 7] class = 0 3->4 49 hours-per-week <= 41.5 entropy = 0.185 samples = 319 value = [310, 9] class = 0 3->49 5 entropy = 0.0 samples = 692 value = [692, 0] class = 0 4->5 6 sex_Male <= 0.5 entropy = 0.061 samples = 983 value = [976, 7] class = 0 4->6 7 workclass_Self-emp <= 0.5 entropy = 0.023 samples = 456 value = [455, 1] class = 0 6->7 12 education <= 9.5 entropy = 0.09 samples = 527 value = [521, 6] class = 0 6->12 8 entropy = 0.0 samples = 450 value = [450, 0] class = 0 7->8 9 age <= 24.0 entropy = 0.65 samples = 6 value = [5, 1] class = 0 7->9 10 entropy = 0.0 samples = 1 value = [0, 1] class = 1 9->10 11 entropy = 0.0 samples = 5 value = [5, 0] class = 0 9->11 13 race_Asian <= 0.5 entropy = 0.031 samples = 316 value = [315, 1] class = 0 12->13 18 hours-per-week <= 27.5 entropy = 0.162 samples = 211 value = [206, 5] class = 0 12->18 14 entropy = 0.0 samples = 307 value = [307, 0] class = 0 13->14 15 education <= 8.0 entropy = 0.503 samples = 9 value = [8, 1] class = 0 13->15 16 entropy = 1.0 samples = 2 value = [1, 1] class = 0 15->16 17 entropy = 0.0 samples = 7 value = [7, 0] class = 0 15->17 19 entropy = 0.0 samples = 43 value = [43, 0] class = 0 18->19 20 race_White <= 0.5 entropy = 0.193 samples = 168 value = [163, 5] class = 0 18->20 21 entropy = 0.0 samples = 36 value = [36, 0] class = 0 20->21 22 age <= 22.5 entropy = 0.232 samples = 132 value = [127, 5] class = 0 20->22 23 entropy = 0.0 samples = 22 value = [22, 0] class = 0 22->23 24 workclass_Private <= 0.5 entropy = 0.267 samples = 110 value = [105, 5] class = 0 22->24 25 entropy = 0.0 samples = 16 value = [16, 0] class = 0 24->25 26 education <= 11.5 entropy = 0.3 samples = 94 value = [89, 5] class = 0 24->26 27 education <= 10.5 entropy = 0.331 samples = 82 value = [77, 5] class = 0 26->27 48 entropy = 0.0 samples = 12 value = [12, 0] class = 0 26->48 28 age <= 27.5 entropy = 0.258 samples = 69 value = [66, 3] class = 0 27->28 41 hours-per-week <= 32.5 entropy = 0.619 samples = 13 value = [11, 2] class = 0 27->41 29 age <= 23.5 entropy = 0.137 samples = 52 value = [51, 1] class = 0 28->29 34 age <= 28.5 entropy = 0.523 samples = 17 value = [15, 2] class = 0 28->34 30 hours-per-week <= 38.5 entropy = 0.297 samples = 19 value = [18, 1] class = 0 29->30 33 entropy = 0.0 samples = 33 value = [33, 0] class = 0 29->33 31 entropy = 0.0 samples = 6 value = [6, 0] class = 0 30->31 32 entropy = 0.391 samples = 13 value = [12, 1] class = 0 30->32 35 entropy = 0.811 samples = 4 value = [3, 1] class = 0 34->35 36 age <= 29.5 entropy = 0.391 samples = 13 value = [12, 1] class = 0 34->36 37 entropy = 0.0 samples = 3 value = [3, 0] class = 0 36->37 38 age <= 30.5 entropy = 0.469 samples = 10 value = [9, 1] class = 0 36->38 39 entropy = 0.544 samples = 8 value = [7, 1] class = 0 38->39 40 entropy = 0.0 samples = 2 value = [2, 0] class = 0 38->40 42 entropy = 0.0 samples = 1 value = [0, 1] class = 1 41->42 43 age <= 25.5 entropy = 0.414 samples = 12 value = [11, 1] class = 0 41->43 44 age <= 24.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 43->44 47 entropy = 0.0 samples = 7 value = [7, 0] class = 0 43->47 45 entropy = 0.0 samples = 3 value = [3, 0] class = 0 44->45 46 entropy = 1.0 samples = 2 value = [1, 1] class = 0 44->46 50 education <= 9.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 49->50 53 workclass_Private <= 0.5 entropy = 0.171 samples = 314 value = [306, 8] class = 0 49->53 51 entropy = 0.0 samples = 3 value = [3, 0] class = 0 50->51 52 entropy = 1.0 samples = 2 value = [1, 1] class = 0 50->52 54 age <= 30.5 entropy = 0.323 samples = 51 value = [48, 3] class = 0 53->54 65 education <= 4.5 entropy = 0.136 samples = 263 value = [258, 5] class = 0 53->65 55 age <= 24.5 entropy = 0.246 samples = 49 value = [47, 2] class = 0 54->55 64 entropy = 1.0 samples = 2 value = [1, 1] class = 0 54->64 56 education <= 9.5 entropy = 0.469 samples = 20 value = [18, 2] class = 0 55->56 63 entropy = 0.0 samples = 29 value = [29, 0] class = 0 55->63 57 race_Black <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 56->57 62 entropy = 0.0 samples = 11 value = [11, 0] class = 0 56->62 58 education <= 8.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 57->58 61 entropy = 0.0 samples = 1 value = [0, 1] class = 1 57->61 59 entropy = 0.918 samples = 3 value = [2, 1] class = 0 58->59 60 entropy = 0.0 samples = 5 value = [5, 0] class = 0 58->60 66 hours-per-week <= 55.0 entropy = 0.414 samples = 12 value = [11, 1] class = 0 65->66 69 age <= 23.5 entropy = 0.118 samples = 251 value = [247, 4] class = 0 65->69 67 entropy = 0.0 samples = 9 value = [9, 0] class = 0 66->67 68 entropy = 0.918 samples = 3 value = [2, 1] class = 0 66->68 70 entropy = 0.0 samples = 87 value = [87, 0] class = 0 69->70 71 hours-per-week <= 50.5 entropy = 0.165 samples = 164 value = [160, 4] class = 0 69->71 72 hours-per-week <= 44.5 entropy = 0.215 samples = 117 value = [113, 4] class = 0 71->72 101 entropy = 0.0 samples = 47 value = [47, 0] class = 0 71->101 73 entropy = 0.0 samples = 20 value = [20, 0] class = 0 72->73 74 age <= 29.5 entropy = 0.248 samples = 97 value = [93, 4] class = 0 72->74 75 education <= 9.5 entropy = 0.177 samples = 75 value = [73, 2] class = 0 74->75 90 age <= 30.5 entropy = 0.439 samples = 22 value = [20, 2] class = 0 74->90 76 age <= 27.5 entropy = 0.297 samples = 38 value = [36, 2] class = 0 75->76 89 entropy = 0.0 samples = 37 value = [37, 0] class = 0 75->89 77 education <= 8.5 entropy = 0.381 samples = 27 value = [25, 2] class = 0 76->77 88 entropy = 0.0 samples = 11 value = [11, 0] class = 0 76->88 78 entropy = 0.0 samples = 6 value = [6, 0] class = 0 77->78 79 age <= 24.5 entropy = 0.454 samples = 21 value = [19, 2] class = 0 77->79 80 sex_Female <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 79->80 83 age <= 26.5 entropy = 0.353 samples = 15 value = [14, 1] class = 0 79->83 81 entropy = 0.0 samples = 3 value = [3, 0] class = 0 80->81 82 entropy = 0.918 samples = 3 value = [2, 1] class = 0 80->82 84 entropy = 0.0 samples = 9 value = [9, 0] class = 0 83->84 85 sex_Female <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 83->85 86 entropy = 0.918 samples = 3 value = [2, 1] class = 0 85->86 87 entropy = 0.0 samples = 3 value = [3, 0] class = 0 85->87 91 sex_Female <= 0.5 entropy = 0.684 samples = 11 value = [9, 2] class = 0 90->91 100 entropy = 0.0 samples = 11 value = [11, 0] class = 0 90->100 92 race_White <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 91->92 99 entropy = 0.0 samples = 2 value = [2, 0] class = 0 91->99 93 entropy = 0.0 samples = 1 value = [1, 0] class = 0 92->93 94 hours-per-week <= 46.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 92->94 95 entropy = 1.0 samples = 2 value = [1, 1] class = 0 94->95 96 education <= 9.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 94->96 97 entropy = 0.0 samples = 2 value = [2, 0] class = 0 96->97 98 entropy = 0.811 samples = 4 value = [3, 1] class = 0 96->98 103 hours-per-week <= 35.5 entropy = 0.227 samples = 1279 value = [1232, 47] class = 0 102->103 318 sex_Male <= 0.5 entropy = 0.491 samples = 402 value = [359, 43] class = 0 102->318 104 workclass_Self-emp <= 0.5 entropy = 0.062 samples = 273 value = [271, 2] class = 0 103->104 111 race_Black <= 0.5 entropy = 0.264 samples = 1006 value = [961, 45] class = 0 103->111 105 entropy = 0.0 samples = 242 value = [242, 0] class = 0 104->105 106 age <= 53.0 entropy = 0.345 samples = 31 value = [29, 2] class = 0 104->106 107 entropy = 0.0 samples = 21 value = [21, 0] class = 0 106->107 108 age <= 55.5 entropy = 0.722 samples = 10 value = [8, 2] class = 0 106->108 109 entropy = 0.0 samples = 2 value = [0, 2] class = 1 108->109 110 entropy = 0.0 samples = 8 value = [8, 0] class = 0 108->110 112 sex_Female <= 0.5 entropy = 0.297 samples = 817 value = [774, 43] class = 0 111->112 305 education <= 10.5 entropy = 0.085 samples = 189 value = [187, 2] class = 0 111->305 113 age <= 50.5 entropy = 0.382 samples = 349 value = [323, 26] class = 0 112->113 222 age <= 34.5 entropy = 0.225 samples = 468 value = [451, 17] class = 0 112->222 114 education <= 10.5 entropy = 0.328 samples = 283 value = [266, 17] class = 0 113->114 189 education <= 11.5 entropy = 0.575 samples = 66 value = [57, 9] class = 0 113->189 115 workclass_Public <= 0.5 entropy = 0.296 samples = 249 value = [236, 13] class = 0 114->115 176 workclass_Public <= 0.5 entropy = 0.523 samples = 34 value = [30, 4] class = 0 114->176 116 education <= 5.5 entropy = 0.323 samples = 204 value = [192, 12] class = 0 115->116 171 age <= 33.5 entropy = 0.154 samples = 45 value = [44, 1] class = 0 115->171 117 entropy = 0.0 samples = 8 value = [8, 0] class = 0 116->117 118 hours-per-week <= 39.0 entropy = 0.332 samples = 196 value = [184, 12] class = 0 116->118 119 entropy = 0.0 samples = 7 value = [7, 0] class = 0 118->119 120 age <= 49.5 entropy = 0.341 samples = 189 value = [177, 12] class = 0 118->120 121 age <= 45.5 entropy = 0.328 samples = 183 value = [172, 11] class = 0 120->121 168 education <= 9.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 120->168 122 age <= 41.5 entropy = 0.36 samples = 161 value = [150, 11] class = 0 121->122 167 entropy = 0.0 samples = 22 value = [22, 0] class = 0 121->167 123 education <= 8.5 entropy = 0.319 samples = 138 value = [130, 8] class = 0 122->123 156 race_Asian <= 0.5 entropy = 0.559 samples = 23 value = [20, 3] class = 0 122->156 124 entropy = 0.0 samples = 16 value = [16, 0] class = 0 123->124 125 race_White <= 0.5 entropy = 0.349 samples = 122 value = [114, 8] class = 0 123->125 126 entropy = 0.0 samples = 5 value = [5, 0] class = 0 125->126 127 education <= 9.5 entropy = 0.36 samples = 117 value = [109, 8] class = 0 125->127 128 age <= 33.5 entropy = 0.317 samples = 87 value = [82, 5] class = 0 127->128 147 age <= 32.5 entropy = 0.469 samples = 30 value = [27, 3] class = 0 127->147 129 entropy = 0.0 samples = 19 value = [19, 0] class = 0 128->129 130 age <= 39.5 entropy = 0.379 samples = 68 value = [63, 5] class = 0 128->130 131 workclass_Self-emp <= 0.5 entropy = 0.475 samples = 49 value = [44, 5] class = 0 130->131 146 entropy = 0.0 samples = 19 value = [19, 0] class = 0 130->146 132 age <= 38.225 entropy = 0.433 samples = 45 value = [41, 4] class = 0 131->132 145 entropy = 0.811 samples = 4 value = [3, 1] class = 0 131->145 133 age <= 34.5 entropy = 0.337 samples = 32 value = [30, 2] class = 0 132->133 142 age <= 38.725 entropy = 0.619 samples = 13 value = [11, 2] class = 0 132->142 134 entropy = 0.65 samples = 6 value = [5, 1] class = 0 133->134 135 age <= 36.5 entropy = 0.235 samples = 26 value = [25, 1] class = 0 133->135 136 entropy = 0.0 samples = 10 value = [10, 0] class = 0 135->136 137 age <= 37.5 entropy = 0.337 samples = 16 value = [15, 1] class = 0 135->137 138 hours-per-week <= 41.0 entropy = 0.469 samples = 10 value = [9, 1] class = 0 137->138 141 entropy = 0.0 samples = 6 value = [6, 0] class = 0 137->141 139 entropy = 0.503 samples = 9 value = [8, 1] class = 0 138->139 140 entropy = 0.0 samples = 1 value = [1, 0] class = 0 138->140 143 entropy = 0.592 samples = 7 value = [6, 1] class = 0 142->143 144 entropy = 0.65 samples = 6 value = [5, 1] class = 0 142->144 148 entropy = 0.918 samples = 3 value = [2, 1] class = 0 147->148 149 age <= 36.5 entropy = 0.381 samples = 27 value = [25, 2] class = 0 147->149 150 entropy = 0.0 samples = 12 value = [12, 0] class = 0 149->150 151 age <= 37.5 entropy = 0.567 samples = 15 value = [13, 2] class = 0 149->151 152 entropy = 0.0 samples = 1 value = [0, 1] class = 1 151->152 153 age <= 40.5 entropy = 0.371 samples = 14 value = [13, 1] class = 0 151->153 154 entropy = 0.0 samples = 11 value = [11, 0] class = 0 153->154 155 entropy = 0.918 samples = 3 value = [2, 1] class = 0 153->155 157 education <= 7.5 entropy = 0.439 samples = 22 value = [20, 2] class = 0 156->157 166 entropy = 0.0 samples = 1 value = [0, 1] class = 1 156->166 158 entropy = 0.0 samples = 1 value = [0, 1] class = 1 157->158 159 education <= 9.5 entropy = 0.276 samples = 21 value = [20, 1] class = 0 157->159 160 age <= 44.5 entropy = 0.414 samples = 12 value = [11, 1] class = 0 159->160 165 entropy = 0.0 samples = 9 value = [9, 0] class = 0 159->165 161 age <= 43.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 160->161 164 entropy = 0.0 samples = 5 value = [5, 0] class = 0 160->164 162 entropy = 0.0 samples = 4 value = [4, 0] class = 0 161->162 163 entropy = 0.918 samples = 3 value = [2, 1] class = 0 161->163 169 entropy = 1.0 samples = 2 value = [1, 1] class = 0 168->169 170 entropy = 0.0 samples = 4 value = [4, 0] class = 0 168->170 172 education <= 9.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 171->172 175 entropy = 0.0 samples = 38 value = [38, 0] class = 0 171->175 173 entropy = 0.0 samples = 4 value = [4, 0] class = 0 172->173 174 entropy = 0.918 samples = 3 value = [2, 1] class = 0 172->174 177 education <= 11.5 entropy = 0.235 samples = 26 value = [25, 1] class = 0 176->177 184 age <= 34.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 176->184 178 entropy = 0.0 samples = 15 value = [15, 0] class = 0 177->178 179 age <= 41.5 entropy = 0.439 samples = 11 value = [10, 1] class = 0 177->179 180 age <= 39.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 179->180 183 entropy = 0.0 samples = 6 value = [6, 0] class = 0 179->183 181 entropy = 0.0 samples = 4 value = [4, 0] class = 0 180->181 182 entropy = 0.0 samples = 1 value = [0, 1] class = 1 180->182 185 entropy = 0.0 samples = 3 value = [3, 0] class = 0 184->185 186 age <= 38.0 entropy = 0.971 samples = 5 value = [2, 3] class = 1 184->186 187 entropy = 0.0 samples = 2 value = [0, 2] class = 1 186->187 188 entropy = 0.918 samples = 3 value = [2, 1] class = 0 186->188 190 race_Hispanic <= 0.5 entropy = 0.538 samples = 65 value = [57, 8] class = 0 189->190 221 entropy = 0.0 samples = 1 value = [0, 1] class = 1 189->221 191 education <= 9.5 entropy = 0.498 samples = 64 value = [57, 7] class = 0 190->191 220 entropy = 0.0 samples = 1 value = [0, 1] class = 1 190->220 192 education <= 4.5 entropy = 0.348 samples = 46 value = [43, 3] class = 0 191->192 207 workclass_Private <= 0.5 entropy = 0.764 samples = 18 value = [14, 4] class = 0 191->207 193 entropy = 0.0 samples = 9 value = [9, 0] class = 0 192->193 194 education <= 5.5 entropy = 0.406 samples = 37 value = [34, 3] class = 0 192->194 195 entropy = 0.918 samples = 3 value = [2, 1] class = 0 194->195 196 age <= 53.5 entropy = 0.323 samples = 34 value = [32, 2] class = 0 194->196 197 education <= 8.5 entropy = 0.567 samples = 15 value = [13, 2] class = 0 196->197 206 entropy = 0.0 samples = 19 value = [19, 0] class = 0 196->206 198 entropy = 0.0 samples = 3 value = [3, 0] class = 0 197->198 199 hours-per-week <= 39.0 entropy = 0.65 samples = 12 value = [10, 2] class = 0 197->199 200 entropy = 0.0 samples = 3 value = [3, 0] class = 0 199->200 201 workclass_Self-emp <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 199->201 202 age <= 52.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 201->202 205 entropy = 1.0 samples = 2 value = [1, 1] class = 0 201->205 203 entropy = 0.0 samples = 4 value = [4, 0] class = 0 202->203 204 entropy = 0.918 samples = 3 value = [2, 1] class = 0 202->204 208 entropy = 0.0 samples = 5 value = [5, 0] class = 0 207->208 209 age <= 57.0 entropy = 0.89 samples = 13 value = [9, 4] class = 0 207->209 210 age <= 55.0 entropy = 0.985 samples = 7 value = [4, 3] class = 0 209->210 217 age <= 59.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 209->217 211 education <= 10.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 210->211 216 entropy = 0.0 samples = 1 value = [0, 1] class = 1 210->216 212 age <= 52.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 211->212 215 entropy = 0.0 samples = 1 value = [1, 0] class = 0 211->215 213 entropy = 1.0 samples = 2 value = [1, 1] class = 0 212->213 214 entropy = 0.918 samples = 3 value = [2, 1] class = 0 212->214 218 entropy = 0.0 samples = 3 value = [3, 0] class = 0 217->218 219 entropy = 0.918 samples = 3 value = [2, 1] class = 0 217->219 223 entropy = 0.0 samples = 60 value = [60, 0] class = 0 222->223 224 age <= 61.5 entropy = 0.25 samples = 408 value = [391, 17] class = 0 222->224 225 race_White <= 0.5 entropy = 0.224 samples = 387 value = [373, 14] class = 0 224->225 294 education <= 9.5 entropy = 0.592 samples = 21 value = [18, 3] class = 0 224->294 226 education <= 9.5 entropy = 0.516 samples = 26 value = [23, 3] class = 0 225->226 237 hours-per-week <= 37.5 entropy = 0.197 samples = 361 value = [350, 11] class = 0 225->237 227 entropy = 0.0 samples = 10 value = [10, 0] class = 0 226->227 228 age <= 45.5 entropy = 0.696 samples = 16 value = [13, 3] class = 0 226->228 229 race_Amer-Indian <= 0.5 entropy = 0.779 samples = 13 value = [10, 3] class = 0 228->229 236 entropy = 0.0 samples = 3 value = [3, 0] class = 0 228->236 230 age <= 40.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 229->230 233 workclass_Private <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 229->233 231 entropy = 0.811 samples = 4 value = [3, 1] class = 0 230->231 232 entropy = 0.0 samples = 1 value = [0, 1] class = 1 230->232 234 entropy = 0.918 samples = 3 value = [2, 1] class = 0 233->234 235 entropy = 0.0 samples = 5 value = [5, 0] class = 0 233->235 238 age <= 40.5 entropy = 0.523 samples = 17 value = [15, 2] class = 0 237->238 247 education <= 10.5 entropy = 0.175 samples = 344 value = [335, 9] class = 0 237->247 239 entropy = 0.0 samples = 6 value = [6, 0] class = 0 238->239 240 age <= 50.0 entropy = 0.684 samples = 11 value = [9, 2] class = 0 238->240 241 education <= 10.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 240->241 246 entropy = 0.0 samples = 4 value = [4, 0] class = 0 240->246 242 age <= 44.0 entropy = 0.918 samples = 6 value = [4, 2] class = 0 241->242 245 entropy = 0.0 samples = 1 value = [1, 0] class = 0 241->245 243 entropy = 1.0 samples = 2 value = [1, 1] class = 0 242->243 244 entropy = 0.811 samples = 4 value = [3, 1] class = 0 242->244 248 age <= 54.5 entropy = 0.141 samples = 300 value = [294, 6] class = 0 247->248 281 age <= 35.5 entropy = 0.359 samples = 44 value = [41, 3] class = 0 247->281 249 age <= 44.5 entropy = 0.094 samples = 250 value = [247, 3] class = 0 248->249 266 age <= 58.5 entropy = 0.327 samples = 50 value = [47, 3] class = 0 248->266 250 age <= 38.225 entropy = 0.143 samples = 148 value = [145, 3] class = 0 249->250 265 entropy = 0.0 samples = 102 value = [102, 0] class = 0 249->265 251 entropy = 0.0 samples = 57 value = [57, 0] class = 0 250->251 252 age <= 39.5 entropy = 0.209 samples = 91 value = [88, 3] class = 0 250->252 253 education <= 7.5 entropy = 0.454 samples = 21 value = [19, 2] class = 0 252->253 260 age <= 43.5 entropy = 0.108 samples = 70 value = [69, 1] class = 0 252->260 254 entropy = 0.918 samples = 3 value = [2, 1] class = 0 253->254 255 education <= 9.5 entropy = 0.31 samples = 18 value = [17, 1] class = 0 253->255 256 entropy = 0.0 samples = 12 value = [12, 0] class = 0 255->256 257 age <= 38.725 entropy = 0.65 samples = 6 value = [5, 1] class = 0 255->257 258 entropy = 0.0 samples = 1 value = [1, 0] class = 0 257->258 259 entropy = 0.722 samples = 5 value = [4, 1] class = 0 257->259 261 entropy = 0.0 samples = 63 value = [63, 0] class = 0 260->261 262 education <= 9.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 260->262 263 entropy = 0.722 samples = 5 value = [4, 1] class = 0 262->263 264 entropy = 0.0 samples = 2 value = [2, 0] class = 0 262->264 267 education <= 8.0 entropy = 0.439 samples = 33 value = [30, 3] class = 0 266->267 280 entropy = 0.0 samples = 17 value = [17, 0] class = 0 266->280 268 entropy = 0.0 samples = 6 value = [6, 0] class = 0 267->268 269 education <= 9.5 entropy = 0.503 samples = 27 value = [24, 3] class = 0 267->269 270 age <= 56.5 entropy = 0.575 samples = 22 value = [19, 3] class = 0 269->270 279 entropy = 0.0 samples = 5 value = [5, 0] class = 0 269->279 271 workclass_Public <= 0.5 entropy = 0.391 samples = 13 value = [12, 1] class = 0 270->271 274 workclass_Public <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 270->274 272 entropy = 0.0 samples = 10 value = [10, 0] class = 0 271->272 273 entropy = 0.918 samples = 3 value = [2, 1] class = 0 271->273 275 age <= 57.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 274->275 278 entropy = 0.0 samples = 2 value = [2, 0] class = 0 274->278 276 entropy = 0.811 samples = 4 value = [3, 1] class = 0 275->276 277 entropy = 0.918 samples = 3 value = [2, 1] class = 0 275->277 282 entropy = 1.0 samples = 2 value = [1, 1] class = 0 281->282 283 age <= 41.5 entropy = 0.276 samples = 42 value = [40, 2] class = 0 281->283 284 entropy = 0.0 samples = 19 value = [19, 0] class = 0 283->284 285 age <= 45.5 entropy = 0.426 samples = 23 value = [21, 2] class = 0 283->285 286 workclass_Private <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 285->286 293 entropy = 0.0 samples = 16 value = [16, 0] class = 0 285->293 287 entropy = 0.0 samples = 1 value = [1, 0] class = 0 286->287 288 hours-per-week <= 39.0 entropy = 0.918 samples = 6 value = [4, 2] class = 0 286->288 289 entropy = 0.0 samples = 1 value = [1, 0] class = 0 288->289 290 age <= 44.0 entropy = 0.971 samples = 5 value = [3, 2] class = 0 288->290 291 entropy = 0.811 samples = 4 value = [3, 1] class = 0 290->291 292 entropy = 0.0 samples = 1 value = [0, 1] class = 1 290->292 295 entropy = 0.0 samples = 10 value = [10, 0] class = 0 294->295 296 age <= 66.5 entropy = 0.845 samples = 11 value = [8, 3] class = 0 294->296 297 age <= 65.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 296->297 304 entropy = 0.0 samples = 4 value = [4, 0] class = 0 296->304 298 hours-per-week <= 39.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 297->298 303 entropy = 0.0 samples = 1 value = [0, 1] class = 1 297->303 299 entropy = 0.0 samples = 1 value = [0, 1] class = 1 298->299 300 education <= 11.0 entropy = 0.722 samples = 5 value = [4, 1] class = 0 298->300 301 entropy = 0.0 samples = 4 value = [4, 0] class = 0 300->301 302 entropy = 0.0 samples = 1 value = [0, 1] class = 1 300->302 306 age <= 46.5 entropy = 0.052 samples = 172 value = [171, 1] class = 0 305->306 315 age <= 33.5 entropy = 0.323 samples = 17 value = [16, 1] class = 0 305->315 307 entropy = 0.0 samples = 115 value = [115, 0] class = 0 306->307 308 age <= 47.5 entropy = 0.127 samples = 57 value = [56, 1] class = 0 306->308 309 workclass_Private <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] class = 0 308->309 314 entropy = 0.0 samples = 46 value = [46, 0] class = 0 308->314 310 entropy = 0.0 samples = 6 value = [6, 0] class = 0 309->310 311 sex_Female <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 309->311 312 entropy = 0.0 samples = 2 value = [2, 0] class = 0 311->312 313 entropy = 0.918 samples = 3 value = [2, 1] class = 0 311->313 316 entropy = 0.918 samples = 3 value = [2, 1] class = 0 315->316 317 entropy = 0.0 samples = 14 value = [14, 0] class = 0 315->317 319 education <= 10.5 entropy = 0.355 samples = 149 value = [139, 10] class = 0 318->319 352 race_White <= 0.5 entropy = 0.559 samples = 253 value = [220, 33] class = 0 318->352 320 hours-per-week <= 51.0 entropy = 0.244 samples = 124 value = [119, 5] class = 0 319->320 341 workclass_Public <= 0.5 entropy = 0.722 samples = 25 value = [20, 5] class = 0 319->341 321 hours-per-week <= 49.5 entropy = 0.337 samples = 80 value = [75, 5] class = 0 320->321 340 entropy = 0.0 samples = 44 value = [44, 0] class = 0 320->340 322 hours-per-week <= 44.5 entropy = 0.144 samples = 49 value = [48, 1] class = 0 321->322 327 age <= 44.5 entropy = 0.555 samples = 31 value = [27, 4] class = 0 321->327 323 age <= 40.5 entropy = 0.414 samples = 12 value = [11, 1] class = 0 322->323 326 entropy = 0.0 samples = 37 value = [37, 0] class = 0 322->326 324 entropy = 0.918 samples = 3 value = [2, 1] class = 0 323->324 325 entropy = 0.0 samples = 9 value = [9, 0] class = 0 323->325 328 age <= 32.5 entropy = 0.297 samples = 19 value = [18, 1] class = 0 327->328 331 age <= 45.5 entropy = 0.811 samples = 12 value = [9, 3] class = 0 327->331 329 entropy = 0.918 samples = 3 value = [2, 1] class = 0 328->329 330 entropy = 0.0 samples = 16 value = [16, 0] class = 0 328->330 332 entropy = 0.0 samples = 1 value = [0, 1] class = 1 331->332 333 age <= 50.5 entropy = 0.684 samples = 11 value = [9, 2] class = 0 331->333 334 entropy = 0.0 samples = 3 value = [3, 0] class = 0 333->334 335 age <= 55.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 333->335 336 age <= 53.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 335->336 339 entropy = 0.0 samples = 3 value = [3, 0] class = 0 335->339 337 entropy = 0.811 samples = 4 value = [3, 1] class = 0 336->337 338 entropy = 0.0 samples = 1 value = [0, 1] class = 1 336->338 342 age <= 38.225 entropy = 0.592 samples = 21 value = [18, 3] class = 0 341->342 351 entropy = 1.0 samples = 4 value = [2, 2] class = 0 341->351 343 age <= 37.5 entropy = 0.881 samples = 10 value = [7, 3] class = 0 342->343 350 entropy = 0.0 samples = 11 value = [11, 0] class = 0 342->350 344 race_Asian <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 343->344 349 entropy = 0.0 samples = 1 value = [0, 1] class = 1 343->349 345 age <= 36.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 344->345 348 entropy = 0.0 samples = 1 value = [0, 1] class = 1 344->348 346 entropy = 0.0 samples = 6 value = [6, 0] class = 0 345->346 347 entropy = 1.0 samples = 2 value = [1, 1] class = 0 345->347 353 entropy = 0.0 samples = 16 value = [16, 0] class = 0 352->353 354 education <= 11.5 entropy = 0.582 samples = 237 value = [204, 33] class = 0 352->354 355 age <= 39.5 entropy = 0.597 samples = 228 value = [195, 33] class = 0 354->355 452 entropy = 0.0 samples = 9 value = [9, 0] class = 0 354->452 356 education <= 8.5 entropy = 0.509 samples = 124 value = [110, 14] class = 0 355->356 405 workclass_Public <= 0.5 entropy = 0.686 samples = 104 value = [85, 19] class = 0 355->405 357 age <= 36.5 entropy = 0.9 samples = 19 value = [13, 6] class = 0 356->357 368 age <= 38.225 entropy = 0.389 samples = 105 value = [97, 8] class = 0 356->368 358 age <= 32.5 entropy = 0.65 samples = 12 value = [10, 2] class = 0 357->358 363 hours-per-week <= 46.5 entropy = 0.985 samples = 7 value = [3, 4] class = 1 357->363 359 entropy = 0.0 samples = 1 value = [0, 1] class = 1 358->359 360 education <= 7.5 entropy = 0.439 samples = 11 value = [10, 1] class = 0 358->360 361 entropy = 0.0 samples = 9 value = [9, 0] class = 0 360->361 362 entropy = 1.0 samples = 2 value = [1, 1] class = 0 360->362 364 entropy = 0.0 samples = 1 value = [1, 0] class = 0 363->364 365 hours-per-week <= 62.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 363->365 366 entropy = 0.0 samples = 3 value = [0, 3] class = 1 365->366 367 entropy = 0.918 samples = 3 value = [2, 1] class = 0 365->367 369 hours-per-week <= 61.0 entropy = 0.436 samples = 89 value = [81, 8] class = 0 368->369 404 entropy = 0.0 samples = 16 value = [16, 0] class = 0 368->404 370 age <= 34.5 entropy = 0.481 samples = 77 value = [69, 8] class = 0 369->370 403 entropy = 0.0 samples = 12 value = [12, 0] class = 0 369->403 371 age <= 33.5 entropy = 0.592 samples = 35 value = [30, 5] class = 0 370->371 388 age <= 35.5 entropy = 0.371 samples = 42 value = [39, 3] class = 0 370->388 372 hours-per-week <= 48.0 entropy = 0.426 samples = 23 value = [21, 2] class = 0 371->372 379 workclass_Public <= 0.5 entropy = 0.811 samples = 12 value = [9, 3] class = 0 371->379 373 entropy = 0.0 samples = 6 value = [6, 0] class = 0 372->373 374 education <= 10.5 entropy = 0.523 samples = 17 value = [15, 2] class = 0 372->374 375 hours-per-week <= 57.5 entropy = 0.353 samples = 15 value = [14, 1] class = 0 374->375 378 entropy = 1.0 samples = 2 value = [1, 1] class = 0 374->378 376 entropy = 0.0 samples = 11 value = [11, 0] class = 0 375->376 377 entropy = 0.811 samples = 4 value = [3, 1] class = 0 375->377 380 hours-per-week <= 44.5 entropy = 0.684 samples = 11 value = [9, 2] class = 0 379->380 387 entropy = 0.0 samples = 1 value = [0, 1] class = 1 379->387 381 entropy = 0.0 samples = 2 value = [2, 0] class = 0 380->381 382 hours-per-week <= 47.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 380->382 383 entropy = 1.0 samples = 2 value = [1, 1] class = 0 382->383 384 education <= 9.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 382->384 385 entropy = 0.0 samples = 3 value = [3, 0] class = 0 384->385 386 entropy = 0.811 samples = 4 value = [3, 1] class = 0 384->386 389 entropy = 0.0 samples = 11 value = [11, 0] class = 0 388->389 390 workclass_Private <= 0.5 entropy = 0.459 samples = 31 value = [28, 3] class = 0 388->390 391 entropy = 0.0 samples = 9 value = [9, 0] class = 0 390->391 392 hours-per-week <= 53.5 entropy = 0.575 samples = 22 value = [19, 3] class = 0 390->392 393 hours-per-week <= 46.5 entropy = 0.371 samples = 14 value = [13, 1] class = 0 392->393 398 hours-per-week <= 56.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 392->398 394 education <= 9.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 393->394 397 entropy = 0.0 samples = 8 value = [8, 0] class = 0 393->397 395 entropy = 0.918 samples = 3 value = [2, 1] class = 0 394->395 396 entropy = 0.0 samples = 3 value = [3, 0] class = 0 394->396 399 entropy = 1.0 samples = 2 value = [1, 1] class = 0 398->399 400 age <= 36.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 398->400 401 entropy = 0.918 samples = 3 value = [2, 1] class = 0 400->401 402 entropy = 0.0 samples = 3 value = [3, 0] class = 0 400->402 406 hours-per-week <= 49.0 entropy = 0.654 samples = 101 value = [84, 17] class = 0 405->406 451 entropy = 0.918 samples = 3 value = [1, 2] class = 1 405->451 407 hours-per-week <= 45.5 entropy = 0.811 samples = 36 value = [27, 9] class = 0 406->407 428 hours-per-week <= 54.5 entropy = 0.538 samples = 65 value = [57, 8] class = 0 406->428 408 age <= 60.5 entropy = 0.619 samples = 26 value = [22, 4] class = 0 407->408 421 education <= 9.5 entropy = 1.0 samples = 10 value = [5, 5] class = 0 407->421 409 hours-per-week <= 44.5 entropy = 0.529 samples = 25 value = [22, 3] class = 0 408->409 420 entropy = 0.0 samples = 1 value = [0, 1] class = 1 408->420 410 entropy = 1.0 samples = 2 value = [1, 1] class = 0 409->410 411 education <= 9.5 entropy = 0.426 samples = 23 value = [21, 2] class = 0 409->411 412 entropy = 0.0 samples = 10 value = [10, 0] class = 0 411->412 413 age <= 43.5 entropy = 0.619 samples = 13 value = [11, 2] class = 0 411->413 414 entropy = 0.0 samples = 5 value = [5, 0] class = 0 413->414 415 age <= 44.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 413->415 416 entropy = 0.0 samples = 1 value = [0, 1] class = 1 415->416 417 age <= 49.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 415->417 418 entropy = 0.811 samples = 4 value = [3, 1] class = 0 417->418 419 entropy = 0.0 samples = 3 value = [3, 0] class = 0 417->419 422 age <= 51.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 421->422 425 hours-per-week <= 47.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 421->425 423 entropy = 0.918 samples = 3 value = [2, 1] class = 0 422->423 424 entropy = 0.0 samples = 2 value = [2, 0] class = 0 422->424 426 entropy = 0.0 samples = 2 value = [0, 2] class = 1 425->426 427 entropy = 0.918 samples = 3 value = [1, 2] class = 1 425->427 429 entropy = 0.0 samples = 28 value = [28, 0] class = 0 428->429 430 age <= 58.0 entropy = 0.753 samples = 37 value = [29, 8] class = 0 428->430 431 hours-per-week <= 72.5 entropy = 0.837 samples = 30 value = [22, 8] class = 0 430->431 450 entropy = 0.0 samples = 7 value = [7, 0] class = 0 430->450 432 age <= 56.5 entropy = 0.877 samples = 27 value = [19, 8] class = 0 431->432 449 entropy = 0.0 samples = 3 value = [3, 0] class = 0 431->449 433 education <= 8.0 entropy = 0.84 samples = 26 value = [19, 7] class = 0 432->433 448 entropy = 0.0 samples = 1 value = [0, 1] class = 1 432->448 434 entropy = 0.0 samples = 3 value = [3, 0] class = 0 433->434 435 age <= 54.0 entropy = 0.887 samples = 23 value = [16, 7] class = 0 433->435 436 hours-per-week <= 65.0 entropy = 0.845 samples = 22 value = [16, 6] class = 0 435->436 447 entropy = 0.0 samples = 1 value = [0, 1] class = 1 435->447 437 workclass_Self-emp <= 0.5 entropy = 0.764 samples = 18 value = [14, 4] class = 0 436->437 446 entropy = 1.0 samples = 4 value = [2, 2] class = 0 436->446 438 hours-per-week <= 56.5 entropy = 0.918 samples = 9 value = [6, 3] class = 0 437->438 443 age <= 47.5 entropy = 0.503 samples = 9 value = [8, 1] class = 0 437->443 439 age <= 49.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 438->439 442 entropy = 0.918 samples = 3 value = [1, 2] class = 1 438->442 440 entropy = 0.0 samples = 4 value = [4, 0] class = 0 439->440 441 entropy = 1.0 samples = 2 value = [1, 1] class = 0 439->441 444 entropy = 0.0 samples = 6 value = [6, 0] class = 0 443->444 445 entropy = 0.918 samples = 3 value = [2, 1] class = 0 443->445 454 hours-per-week <= 39.5 entropy = 0.228 samples = 352 value = [339, 13] class = 0 453->454 515 education <= 14.5 entropy = 0.879 samples = 648 value = [455, 193] class = 0 453->515 455 entropy = 0.0 samples = 87 value = [87, 0] class = 0 454->455 456 workclass_Public <= 0.5 entropy = 0.282 samples = 265 value = [252, 13] class = 0 454->456 457 education <= 13.5 entropy = 0.323 samples = 221 value = [208, 13] class = 0 456->457 514 entropy = 0.0 samples = 44 value = [44, 0] class = 0 456->514 458 age <= 22.5 entropy = 0.268 samples = 197 value = [188, 9] class = 0 457->458 499 education <= 15.5 entropy = 0.65 samples = 24 value = [20, 4] class = 0 457->499 459 hours-per-week <= 52.5 entropy = 0.619 samples = 13 value = [11, 2] class = 0 458->459 466 sex_Female <= 0.5 entropy = 0.233 samples = 184 value = [177, 7] class = 0 458->466 460 sex_Male <= 0.5 entropy = 0.469 samples = 10 value = [9, 1] class = 0 459->460 465 entropy = 0.918 samples = 3 value = [2, 1] class = 0 459->465 461 hours-per-week <= 45.0 entropy = 0.722 samples = 5 value = [4, 1] class = 0 460->461 464 entropy = 0.0 samples = 5 value = [5, 0] class = 0 460->464 462 entropy = 0.811 samples = 4 value = [3, 1] class = 0 461->462 463 entropy = 0.0 samples = 1 value = [1, 0] class = 0 461->463 467 hours-per-week <= 49.0 entropy = 0.318 samples = 104 value = [98, 6] class = 0 466->467 490 age <= 25.5 entropy = 0.097 samples = 80 value = [79, 1] class = 0 466->490 468 race_Asian <= 0.5 entropy = 0.238 samples = 77 value = [74, 3] class = 0 467->468 481 age <= 24.5 entropy = 0.503 samples = 27 value = [24, 3] class = 0 467->481 469 workclass_Private <= 0.5 entropy = 0.181 samples = 73 value = [71, 2] class = 0 468->469 480 entropy = 0.811 samples = 4 value = [3, 1] class = 0 468->480 470 age <= 23.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 469->470 473 age <= 27.5 entropy = 0.112 samples = 67 value = [66, 1] class = 0 469->473 471 entropy = 0.0 samples = 1 value = [0, 1] class = 1 470->471 472 entropy = 0.0 samples = 5 value = [5, 0] class = 0 470->472 474 entropy = 0.0 samples = 49 value = [49, 0] class = 0 473->474 475 age <= 28.5 entropy = 0.31 samples = 18 value = [17, 1] class = 0 473->475 476 hours-per-week <= 41.5 entropy = 0.503 samples = 9 value = [8, 1] class = 0 475->476 479 entropy = 0.0 samples = 9 value = [9, 0] class = 0 475->479 477 entropy = 0.722 samples = 5 value = [4, 1] class = 0 476->477 478 entropy = 0.0 samples = 4 value = [4, 0] class = 0 476->478 482 entropy = 0.0 samples = 11 value = [11, 0] class = 0 481->482 483 hours-per-week <= 52.5 entropy = 0.696 samples = 16 value = [13, 3] class = 0 481->483 484 age <= 26.5 entropy = 0.881 samples = 10 value = [7, 3] class = 0 483->484 489 entropy = 0.0 samples = 6 value = [6, 0] class = 0 483->489 485 entropy = 0.918 samples = 3 value = [1, 2] class = 1 484->485 486 age <= 28.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 484->486 487 entropy = 0.0 samples = 4 value = [4, 0] class = 0 486->487 488 entropy = 0.918 samples = 3 value = [2, 1] class = 0 486->488 491 age <= 24.5 entropy = 0.176 samples = 38 value = [37, 1] class = 0 490->491 498 entropy = 0.0 samples = 42 value = [42, 0] class = 0 490->498 492 entropy = 0.0 samples = 26 value = [26, 0] class = 0 491->492 493 race_Asian <= 0.5 entropy = 0.414 samples = 12 value = [11, 1] class = 0 491->493 494 hours-per-week <= 45.0 entropy = 0.469 samples = 10 value = [9, 1] class = 0 493->494 497 entropy = 0.0 samples = 2 value = [2, 0] class = 0 493->497 495 entropy = 0.544 samples = 8 value = [7, 1] class = 0 494->495 496 entropy = 0.0 samples = 2 value = [2, 0] class = 0 494->496 500 age <= 25.5 entropy = 0.742 samples = 19 value = [15, 4] class = 0 499->500 513 entropy = 0.0 samples = 5 value = [5, 0] class = 0 499->513 501 entropy = 0.0 samples = 4 value = [4, 0] class = 0 500->501 502 age <= 27.5 entropy = 0.837 samples = 15 value = [11, 4] class = 0 500->502 503 hours-per-week <= 57.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 502->503 506 hours-per-week <= 52.5 entropy = 0.722 samples = 10 value = [8, 2] class = 0 502->506 504 entropy = 0.811 samples = 4 value = [3, 1] class = 0 503->504 505 entropy = 0.0 samples = 1 value = [0, 1] class = 1 503->505 507 education <= 14.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 506->507 512 entropy = 0.0 samples = 3 value = [3, 0] class = 0 506->512 508 sex_Male <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 507->508 511 entropy = 0.0 samples = 1 value = [0, 1] class = 1 507->511 509 entropy = 0.918 samples = 3 value = [2, 1] class = 0 508->509 510 entropy = 0.0 samples = 3 value = [3, 0] class = 0 508->510 516 hours-per-week <= 42.5 entropy = 0.821 samples = 582 value = [433, 149] class = 0 515->516 861 hours-per-week <= 75.0 entropy = 0.918 samples = 66 value = [22, 44] class = 1 515->861 517 age <= 44.5 entropy = 0.666 samples = 351 value = [290, 61] class = 0 516->517 696 age <= 49.5 entropy = 0.959 samples = 231 value = [143, 88] class = 0 516->696 518 hours-per-week <= 10.0 entropy = 0.557 samples = 239 value = [208, 31] class = 0 517->518 623 hours-per-week <= 39.0 entropy = 0.838 samples = 112 value = [82, 30] class = 0 517->623 519 entropy = 0.0 samples = 1 value = [0, 1] class = 1 518->519 520 hours-per-week <= 31.0 entropy = 0.547 samples = 238 value = [208, 30] class = 0 518->520 521 entropy = 0.0 samples = 21 value = [21, 0] class = 0 520->521 522 age <= 37.5 entropy = 0.58 samples = 217 value = [187, 30] class = 0 520->522 523 age <= 33.5 entropy = 0.653 samples = 113 value = [94, 19] class = 0 522->523 582 age <= 38.725 entropy = 0.487 samples = 104 value = [93, 11] class = 0 522->582 524 sex_Male <= 0.5 entropy = 0.451 samples = 53 value = [48, 5] class = 0 523->524 543 workclass_Public <= 0.5 entropy = 0.784 samples = 60 value = [46, 14] class = 0 523->543 525 age <= 30.5 entropy = 0.216 samples = 29 value = [28, 1] class = 0 524->525 530 workclass_Private <= 0.5 entropy = 0.65 samples = 24 value = [20, 4] class = 0 524->530 526 workclass_Private <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] class = 0 525->526 529 entropy = 0.0 samples = 18 value = [18, 0] class = 0 525->529 527 entropy = 0.0 samples = 5 value = [5, 0] class = 0 526->527 528 entropy = 0.65 samples = 6 value = [5, 1] class = 0 526->528 531 race_White <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 530->531 534 race_White <= 0.5 entropy = 0.485 samples = 19 value = [17, 2] class = 0 530->534 532 entropy = 0.0 samples = 1 value = [0, 1] class = 1 531->532 533 entropy = 0.811 samples = 4 value = [3, 1] class = 0 531->533 535 entropy = 0.0 samples = 5 value = [5, 0] class = 0 534->535 536 hours-per-week <= 39.0 entropy = 0.592 samples = 14 value = [12, 2] class = 0 534->536 537 entropy = 0.0 samples = 2 value = [2, 0] class = 0 536->537 538 age <= 31.5 entropy = 0.65 samples = 12 value = [10, 2] class = 0 536->538 539 entropy = 0.918 samples = 3 value = [2, 1] class = 0 538->539 540 age <= 32.5 entropy = 0.503 samples = 9 value = [8, 1] class = 0 538->540 541 entropy = 0.0 samples = 5 value = [5, 0] class = 0 540->541 542 entropy = 0.811 samples = 4 value = [3, 1] class = 0 540->542 544 education <= 13.5 entropy = 0.863 samples = 42 value = [30, 12] class = 0 543->544 575 hours-per-week <= 39.0 entropy = 0.503 samples = 18 value = [16, 2] class = 0 543->575 545 hours-per-week <= 41.0 entropy = 0.822 samples = 35 value = [26, 9] class = 0 544->545 570 race_White <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 544->570 546 hours-per-week <= 33.5 entropy = 0.834 samples = 34 value = [25, 9] class = 0 545->546 569 entropy = 0.0 samples = 1 value = [1, 0] class = 0 545->569 547 entropy = 1.0 samples = 2 value = [1, 1] class = 0 546->547 548 hours-per-week <= 39.0 entropy = 0.811 samples = 32 value = [24, 8] class = 0 546->548 549 entropy = 0.0 samples = 4 value = [4, 0] class = 0 548->549 550 sex_Male <= 0.5 entropy = 0.863 samples = 28 value = [20, 8] class = 0 548->550 551 workclass_Self-emp <= 0.5 entropy = 0.742 samples = 19 value = [15, 4] class = 0 550->551 562 workclass_Private <= 0.5 entropy = 0.991 samples = 9 value = [5, 4] class = 0 550->562 552 age <= 35.5 entropy = 0.65 samples = 18 value = [15, 3] class = 0 551->552 561 entropy = 0.0 samples = 1 value = [0, 1] class = 1 551->561 553 race_Black <= 0.5 entropy = 0.845 samples = 11 value = [8, 3] class = 0 552->553 560 entropy = 0.0 samples = 7 value = [7, 0] class = 0 552->560 554 race_Asian <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] class = 0 553->554 559 entropy = 0.0 samples = 1 value = [1, 0] class = 0 553->559 555 age <= 34.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 554->555 558 entropy = 1.0 samples = 2 value = [1, 1] class = 0 554->558 556 entropy = 0.918 samples = 3 value = [2, 1] class = 0 555->556 557 entropy = 0.722 samples = 5 value = [4, 1] class = 0 555->557 563 entropy = 0.0 samples = 3 value = [3, 0] class = 0 562->563 564 age <= 36.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 562->564 565 age <= 34.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 564->565 568 entropy = 0.0 samples = 1 value = [0, 1] class = 1 564->568 566 entropy = 1.0 samples = 2 value = [1, 1] class = 0 565->566 567 entropy = 0.918 samples = 3 value = [1, 2] class = 1 565->567 571 entropy = 0.0 samples = 2 value = [2, 0] class = 0 570->571 572 age <= 35.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 570->572 573 entropy = 0.0 samples = 2 value = [0, 2] class = 1 572->573 574 entropy = 0.918 samples = 3 value = [2, 1] class = 0 572->574 576 entropy = 0.0 samples = 5 value = [5, 0] class = 0 575->576 577 age <= 36.5 entropy = 0.619 samples = 13 value = [11, 2] class = 0 575->577 578 sex_Male <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] class = 0 577->578 581 entropy = 1.0 samples = 2 value = [1, 1] class = 0 577->581 579 entropy = 0.0 samples = 7 value = [7, 0] class = 0 578->579 580 entropy = 0.811 samples = 4 value = [3, 1] class = 0 578->580 583 entropy = 0.0 samples = 19 value = [19, 0] class = 0 582->583 584 workclass_Self-emp <= 0.5 entropy = 0.556 samples = 85 value = [74, 11] class = 0 582->584 585 hours-per-week <= 38.5 entropy = 0.592 samples = 77 value = [66, 11] class = 0 584->585 622 entropy = 0.0 samples = 8 value = [8, 0] class = 0 584->622 586 sex_Female <= 0.5 entropy = 0.811 samples = 12 value = [9, 3] class = 0 585->586 595 age <= 42.5 entropy = 0.538 samples = 65 value = [57, 8] class = 0 585->595 587 entropy = 0.0 samples = 4 value = [4, 0] class = 0 586->587 588 age <= 40.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 586->588 589 entropy = 0.0 samples = 2 value = [2, 0] class = 0 588->589 590 hours-per-week <= 33.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 588->590 591 entropy = 0.0 samples = 1 value = [1, 0] class = 0 590->591 592 workclass_Private <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 590->592 593 entropy = 0.918 samples = 3 value = [2, 1] class = 0 592->593 594 entropy = 0.0 samples = 2 value = [0, 2] class = 1 592->594 596 age <= 40.5 entropy = 0.391 samples = 39 value = [36, 3] class = 0 595->596 609 sex_Female <= 0.5 entropy = 0.706 samples = 26 value = [21, 5] class = 0 595->609 597 education <= 13.5 entropy = 0.696 samples = 16 value = [13, 3] class = 0 596->597 608 entropy = 0.0 samples = 23 value = [23, 0] class = 0 596->608 598 race_White <= 0.5 entropy = 0.811 samples = 12 value = [9, 3] class = 0 597->598 607 entropy = 0.0 samples = 4 value = [4, 0] class = 0 597->607 599 entropy = 0.0 samples = 1 value = [1, 0] class = 0 598->599 600 sex_Female <= 0.5 entropy = 0.845 samples = 11 value = [8, 3] class = 0 598->600 601 workclass_Private <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 600->601 604 workclass_Public <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 600->604 602 entropy = 1.0 samples = 2 value = [1, 1] class = 0 601->602 603 entropy = 0.811 samples = 4 value = [3, 1] class = 0 601->603 605 entropy = 0.918 samples = 3 value = [2, 1] class = 0 604->605 606 entropy = 0.0 samples = 2 value = [2, 0] class = 0 604->606 610 race_White <= 0.5 entropy = 0.863 samples = 14 value = [10, 4] class = 0 609->610 619 race_White <= 0.5 entropy = 0.414 samples = 12 value = [11, 1] class = 0 609->619 611 entropy = 0.0 samples = 2 value = [2, 0] class = 0 610->611 612 education <= 13.5 entropy = 0.918 samples = 12 value = [8, 4] class = 0 610->612 613 age <= 43.5 entropy = 0.971 samples = 10 value = [6, 4] class = 0 612->613 618 entropy = 0.0 samples = 2 value = [2, 0] class = 0 612->618 614 workclass_Public <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 613->614 617 entropy = 0.811 samples = 4 value = [3, 1] class = 0 613->617 615 entropy = 0.811 samples = 4 value = [3, 1] class = 0 614->615 616 entropy = 0.0 samples = 2 value = [0, 2] class = 1 614->616 620 entropy = 1.0 samples = 2 value = [1, 1] class = 0 619->620 621 entropy = 0.0 samples = 10 value = [10, 0] class = 0 619->621 624 age <= 74.5 entropy = 0.431 samples = 34 value = [31, 3] class = 0 623->624 635 workclass_Private <= 0.5 entropy = 0.931 samples = 78 value = [51, 27] class = 0 623->635 625 hours-per-week <= 33.5 entropy = 0.33 samples = 33 value = [31, 2] class = 0 624->625 634 entropy = 0.0 samples = 1 value = [0, 1] class = 1 624->634 626 entropy = 0.0 samples = 20 value = [20, 0] class = 0 625->626 627 hours-per-week <= 35.5 entropy = 0.619 samples = 13 value = [11, 2] class = 0 625->627 628 workclass_Private <= 0.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 627->628 633 entropy = 0.0 samples = 5 value = [5, 0] class = 0 627->633 629 entropy = 0.0 samples = 1 value = [0, 1] class = 1 628->629 630 age <= 46.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 628->630 631 entropy = 1.0 samples = 2 value = [1, 1] class = 0 630->631 632 entropy = 0.0 samples = 5 value = [5, 0] class = 0 630->632 636 race_Asian <= 0.5 entropy = 0.985 samples = 35 value = [20, 15] class = 0 635->636 665 age <= 60.5 entropy = 0.854 samples = 43 value = [31, 12] class = 0 635->665 637 hours-per-week <= 41.0 entropy = 0.977 samples = 34 value = [20, 14] class = 0 636->637 664 entropy = 0.0 samples = 1 value = [0, 1] class = 1 636->664 638 age <= 45.5 entropy = 0.967 samples = 33 value = [20, 13] class = 0 637->638 663 entropy = 0.0 samples = 1 value = [0, 1] class = 1 637->663 639 entropy = 0.0 samples = 4 value = [4, 0] class = 0 638->639 640 age <= 54.5 entropy = 0.992 samples = 29 value = [16, 13] class = 0 638->640 641 age <= 52.0 entropy = 0.998 samples = 21 value = [10, 11] class = 1 640->641 660 age <= 61.0 entropy = 0.811 samples = 8 value = [6, 2] class = 0 640->660 642 age <= 50.5 entropy = 1.0 samples = 20 value = [10, 10] class = 0 641->642 659 entropy = 0.0 samples = 1 value = [0, 1] class = 1 641->659 643 age <= 49.5 entropy = 0.998 samples = 19 value = [9, 10] class = 1 642->643 658 entropy = 0.0 samples = 1 value = [1, 0] class = 0 642->658 644 age <= 48.5 entropy = 0.997 samples = 15 value = [8, 7] class = 0 643->644 657 entropy = 0.811 samples = 4 value = [1, 3] class = 1 643->657 645 age <= 47.5 entropy = 0.971 samples = 10 value = [4, 6] class = 1 644->645 654 race_White <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 644->654 646 education <= 13.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 645->646 653 entropy = 0.0 samples = 2 value = [0, 2] class = 1 645->653 647 race_White <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 646->647 652 entropy = 0.0 samples = 2 value = [2, 0] class = 0 646->652 648 entropy = 0.0 samples = 1 value = [1, 0] class = 0 647->648 649 age <= 46.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 647->649 650 entropy = 0.918 samples = 3 value = [1, 2] class = 1 649->650 651 entropy = 0.0 samples = 2 value = [0, 2] class = 1 649->651 655 entropy = 0.918 samples = 3 value = [2, 1] class = 0 654->655 656 entropy = 0.0 samples = 2 value = [2, 0] class = 0 654->656 661 entropy = 0.0 samples = 4 value = [4, 0] class = 0 660->661 662 entropy = 1.0 samples = 4 value = [2, 2] class = 0 660->662 666 age <= 58.5 entropy = 0.8 samples = 37 value = [28, 9] class = 0 665->666 691 age <= 62.0 entropy = 1.0 samples = 6 value = [3, 3] class = 0 665->691 667 age <= 57.5 entropy = 0.834 samples = 34 value = [25, 9] class = 0 666->667 690 entropy = 0.0 samples = 3 value = [3, 0] class = 0 666->690 668 sex_Female <= 0.5 entropy = 0.799 samples = 33 value = [25, 8] class = 0 667->668 689 entropy = 0.0 samples = 1 value = [0, 1] class = 1 667->689 669 age <= 46.5 entropy = 0.954 samples = 16 value = [10, 6] class = 0 668->669 684 education <= 13.5 entropy = 0.523 samples = 17 value = [15, 2] class = 0 668->684 670 age <= 45.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 669->670 673 hours-per-week <= 41.0 entropy = 0.994 samples = 11 value = [6, 5] class = 0 669->673 671 entropy = 0.918 samples = 3 value = [2, 1] class = 0 670->671 672 entropy = 0.0 samples = 2 value = [2, 0] class = 0 670->672 674 education <= 13.5 entropy = 1.0 samples = 10 value = [5, 5] class = 0 673->674 683 entropy = 0.0 samples = 1 value = [1, 0] class = 0 673->683 675 age <= 52.5 entropy = 0.991 samples = 9 value = [5, 4] class = 0 674->675 682 entropy = 0.0 samples = 1 value = [0, 1] class = 1 674->682 676 age <= 51.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 675->676 681 entropy = 0.0 samples = 1 value = [1, 0] class = 0 675->681 677 age <= 50.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 676->677 680 entropy = 0.0 samples = 1 value = [0, 1] class = 1 676->680 678 entropy = 0.918 samples = 3 value = [2, 1] class = 0 677->678 679 entropy = 1.0 samples = 4 value = [2, 2] class = 0 677->679 685 entropy = 0.0 samples = 10 value = [10, 0] class = 0 684->685 686 age <= 50.0 entropy = 0.863 samples = 7 value = [5, 2] class = 0 684->686 687 entropy = 0.918 samples = 3 value = [1, 2] class = 1 686->687 688 entropy = 0.0 samples = 4 value = [4, 0] class = 0 686->688 692 entropy = 0.0 samples = 1 value = [0, 1] class = 1 691->692 693 education <= 13.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 691->693 694 entropy = 0.0 samples = 2 value = [2, 0] class = 0 693->694 695 entropy = 0.918 samples = 3 value = [1, 2] class = 1 693->695 697 education <= 13.5 entropy = 0.925 samples = 188 value = [124, 64] class = 0 696->697 834 age <= 56.5 entropy = 0.99 samples = 43 value = [19, 24] class = 1 696->834 698 race_Hispanic <= 0.5 entropy = 0.881 samples = 130 value = [91, 39] class = 0 697->698 787 workclass_Public <= 0.5 entropy = 0.986 samples = 58 value = [33, 25] class = 0 697->787 699 age <= 43.5 entropy = 0.875 samples = 129 value = [91, 38] class = 0 698->699 786 entropy = 0.0 samples = 1 value = [0, 1] class = 1 698->786 700 hours-per-week <= 67.5 entropy = 0.823 samples = 97 value = [72, 25] class = 0 699->700 765 hours-per-week <= 47.0 entropy = 0.974 samples = 32 value = [19, 13] class = 0 699->765 701 age <= 38.225 entropy = 0.836 samples = 94 value = [69, 25] class = 0 700->701 764 entropy = 0.0 samples = 3 value = [3, 0] class = 0 700->764 702 age <= 36.5 entropy = 0.877 samples = 64 value = [45, 19] class = 0 701->702 745 hours-per-week <= 46.5 entropy = 0.722 samples = 30 value = [24, 6] class = 0 701->745 703 age <= 35.5 entropy = 0.838 samples = 56 value = [41, 15] class = 0 702->703 742 sex_Male <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 702->742 704 workclass_Self-emp <= 0.5 entropy = 0.879 samples = 47 value = [33, 14] class = 0 703->704 739 race_Black <= 0.5 entropy = 0.503 samples = 9 value = [8, 1] class = 0 703->739 705 hours-per-week <= 49.0 entropy = 0.902 samples = 44 value = [30, 14] class = 0 704->705 738 entropy = 0.0 samples = 3 value = [3, 0] class = 0 704->738 706 age <= 30.5 entropy = 0.742 samples = 19 value = [15, 4] class = 0 705->706 715 race_Asian <= 0.5 entropy = 0.971 samples = 25 value = [15, 10] class = 0 705->715 707 entropy = 1.0 samples = 4 value = [2, 2] class = 0 706->707 708 age <= 32.5 entropy = 0.567 samples = 15 value = [13, 2] class = 0 706->708 709 entropy = 0.0 samples = 8 value = [8, 0] class = 0 708->709 710 workclass_Private <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 708->710 711 entropy = 0.0 samples = 2 value = [2, 0] class = 0 710->711 712 age <= 34.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 710->712 713 entropy = 0.811 samples = 4 value = [3, 1] class = 0 712->713 714 entropy = 0.0 samples = 1 value = [0, 1] class = 1 712->714 716 sex_Male <= 0.5 entropy = 0.954 samples = 24 value = [15, 9] class = 0 715->716 737 entropy = 0.0 samples = 1 value = [0, 1] class = 1 715->737 717 race_Black <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 716->717 726 race_White <= 0.5 entropy = 0.896 samples = 16 value = [11, 5] class = 0 716->726 718 age <= 30.5 entropy = 0.985 samples = 7 value = [3, 4] class = 1 717->718 725 entropy = 0.0 samples = 1 value = [1, 0] class = 0 717->725 719 entropy = 0.0 samples = 1 value = [1, 0] class = 0 718->719 720 age <= 34.0 entropy = 0.918 samples = 6 value = [2, 4] class = 1 718->720 721 workclass_Private <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 720->721 724 entropy = 0.0 samples = 1 value = [1, 0] class = 0 720->724 722 entropy = 0.918 samples = 3 value = [1, 2] class = 1 721->722 723 entropy = 0.0 samples = 2 value = [0, 2] class = 1 721->723 727 entropy = 0.0 samples = 1 value = [0, 1] class = 1 726->727 728 age <= 34.5 entropy = 0.837 samples = 15 value = [11, 4] class = 0 726->728 729 age <= 31.5 entropy = 0.722 samples = 10 value = [8, 2] class = 0 728->729 736 entropy = 0.971 samples = 5 value = [3, 2] class = 0 728->736 730 workclass_Public <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 729->730 735 entropy = 0.0 samples = 3 value = [3, 0] class = 0 729->735 731 hours-per-week <= 52.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 730->731 734 entropy = 0.0 samples = 1 value = [0, 1] class = 1 730->734 732 entropy = 0.0 samples = 4 value = [4, 0] class = 0 731->732 733 entropy = 1.0 samples = 2 value = [1, 1] class = 0 731->733 740 entropy = 0.0 samples = 7 value = [7, 0] class = 0 739->740 741 entropy = 1.0 samples = 2 value = [1, 1] class = 0 739->741 743 entropy = 0.811 samples = 4 value = [3, 1] class = 0 742->743 744 entropy = 0.811 samples = 4 value = [1, 3] class = 1 742->744 746 workclass_Public <= 0.5 entropy = 0.991 samples = 9 value = [5, 4] class = 0 745->746 755 workclass_Private <= 0.5 entropy = 0.454 samples = 21 value = [19, 2] class = 0 745->755 747 hours-per-week <= 44.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 746->747 754 entropy = 0.0 samples = 1 value = [0, 1] class = 1 746->754 748 entropy = 0.0 samples = 1 value = [0, 1] class = 1 747->748 749 age <= 39.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 747->749 750 entropy = 0.0 samples = 1 value = [1, 0] class = 0 749->750 751 sex_Female <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 749->751 752 entropy = 0.811 samples = 4 value = [3, 1] class = 0 751->752 753 entropy = 1.0 samples = 2 value = [1, 1] class = 0 751->753 756 entropy = 0.0 samples = 7 value = [7, 0] class = 0 755->756 757 age <= 39.5 entropy = 0.592 samples = 14 value = [12, 2] class = 0 755->757 758 entropy = 0.0 samples = 5 value = [5, 0] class = 0 757->758 759 age <= 41.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 757->759 760 race_White <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 759->760 763 entropy = 0.0 samples = 3 value = [3, 0] class = 0 759->763 761 entropy = 0.0 samples = 2 value = [2, 0] class = 0 760->761 762 entropy = 1.0 samples = 4 value = [2, 2] class = 0 760->762 766 sex_Female <= 0.5 entropy = 0.684 samples = 11 value = [9, 2] class = 0 765->766 773 race_White <= 0.5 entropy = 0.998 samples = 21 value = [10, 11] class = 1 765->773 767 race_White <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 766->767 772 entropy = 0.0 samples = 5 value = [5, 0] class = 0 766->772 768 entropy = 0.0 samples = 1 value = [0, 1] class = 1 767->768 769 age <= 46.0 entropy = 0.722 samples = 5 value = [4, 1] class = 0 767->769 770 entropy = 1.0 samples = 2 value = [1, 1] class = 0 769->770 771 entropy = 0.0 samples = 3 value = [3, 0] class = 0 769->771 774 entropy = 0.0 samples = 2 value = [2, 0] class = 0 773->774 775 sex_Female <= 0.5 entropy = 0.982 samples = 19 value = [8, 11] class = 1 773->775 776 age <= 47.5 entropy = 0.592 samples = 7 value = [1, 6] class = 1 775->776 779 workclass_Private <= 0.5 entropy = 0.98 samples = 12 value = [7, 5] class = 0 775->779 777 entropy = 0.0 samples = 5 value = [0, 5] class = 1 776->777 778 entropy = 1.0 samples = 2 value = [1, 1] class = 0 776->778 780 age <= 47.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 779->780 783 hours-per-week <= 55.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 779->783 781 entropy = 1.0 samples = 4 value = [2, 2] class = 0 780->781 782 entropy = 0.0 samples = 3 value = [3, 0] class = 0 780->782 784 entropy = 0.918 samples = 3 value = [2, 1] class = 0 783->784 785 entropy = 0.0 samples = 2 value = [0, 2] class = 1 783->785 788 age <= 36.5 entropy = 0.995 samples = 35 value = [16, 19] class = 1 787->788 819 age <= 39.5 entropy = 0.828 samples = 23 value = [17, 6] class = 0 787->819 789 age <= 34.5 entropy = 0.764 samples = 9 value = [2, 7] class = 1 788->789 792 hours-per-week <= 77.5 entropy = 0.996 samples = 26 value = [14, 12] class = 0 788->792 790 entropy = 1.0 samples = 4 value = [2, 2] class = 0 789->790 791 entropy = 0.0 samples = 5 value = [0, 5] class = 1 789->791 793 age <= 48.0 entropy = 0.99 samples = 25 value = [14, 11] class = 0 792->793 818 entropy = 0.0 samples = 1 value = [0, 1] class = 1 792->818 794 race_Black <= 0.5 entropy = 0.999 samples = 23 value = [12, 11] class = 0 793->794 817 entropy = 0.0 samples = 2 value = [2, 0] class = 0 793->817 795 hours-per-week <= 44.0 entropy = 0.994 samples = 22 value = [12, 10] class = 0 794->795 816 entropy = 0.0 samples = 1 value = [0, 1] class = 1 794->816 796 entropy = 0.0 samples = 1 value = [1, 0] class = 0 795->796 797 hours-per-week <= 62.5 entropy = 0.998 samples = 21 value = [11, 10] class = 0 795->797 798 age <= 38.725 entropy = 1.0 samples = 20 value = [10, 10] class = 0 797->798 815 entropy = 0.0 samples = 1 value = [1, 0] class = 0 797->815 799 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 798->799 804 hours-per-week <= 46.5 entropy = 0.985 samples = 14 value = [8, 6] class = 0 798->804 800 sex_Female <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 799->800 803 entropy = 0.0 samples = 1 value = [0, 1] class = 1 799->803 801 entropy = 0.918 samples = 3 value = [1, 2] class = 1 800->801 802 entropy = 1.0 samples = 2 value = [1, 1] class = 0 800->802 805 entropy = 0.0 samples = 2 value = [2, 0] class = 0 804->805 806 age <= 44.5 entropy = 1.0 samples = 12 value = [6, 6] class = 0 804->806 807 entropy = 0.811 samples = 4 value = [3, 1] class = 0 806->807 808 sex_Female <= 0.5 entropy = 0.954 samples = 8 value = [3, 5] class = 1 806->808 809 entropy = 0.0 samples = 2 value = [0, 2] class = 1 808->809 810 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 808->810 811 age <= 46.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 810->811 814 entropy = 0.0 samples = 1 value = [1, 0] class = 0 810->814 812 entropy = 1.0 samples = 4 value = [2, 2] class = 0 811->812 813 entropy = 0.0 samples = 1 value = [0, 1] class = 1 811->813 820 entropy = 0.0 samples = 6 value = [6, 0] class = 0 819->820 821 race_Black <= 0.5 entropy = 0.937 samples = 17 value = [11, 6] class = 0 819->821 822 hours-per-week <= 53.5 entropy = 0.896 samples = 16 value = [11, 5] class = 0 821->822 833 entropy = 0.0 samples = 1 value = [0, 1] class = 1 821->833 823 age <= 46.0 entropy = 0.544 samples = 8 value = [7, 1] class = 0 822->823 826 age <= 40.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 822->826 824 entropy = 0.0 samples = 4 value = [4, 0] class = 0 823->824 825 entropy = 0.811 samples = 4 value = [3, 1] class = 0 823->825 827 entropy = 0.0 samples = 1 value = [0, 1] class = 1 826->827 828 age <= 41.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 826->828 829 entropy = 0.0 samples = 2 value = [2, 0] class = 0 828->829 830 hours-per-week <= 58.0 entropy = 0.971 samples = 5 value = [2, 3] class = 1 828->830 831 entropy = 0.0 samples = 1 value = [0, 1] class = 1 830->831 832 entropy = 1.0 samples = 4 value = [2, 2] class = 0 830->832 835 race_Amer-Indian <= 0.5 entropy = 0.881 samples = 30 value = [9, 21] class = 1 834->835 856 sex_Female <= 0.5 entropy = 0.779 samples = 13 value = [10, 3] class = 0 834->856 836 hours-per-week <= 57.5 entropy = 0.811 samples = 28 value = [7, 21] class = 1 835->836 855 entropy = 0.0 samples = 2 value = [2, 0] class = 0 835->855 837 education <= 13.5 entropy = 0.503 samples = 18 value = [2, 16] class = 1 836->837 850 workclass_Private <= 0.5 entropy = 1.0 samples = 10 value = [5, 5] class = 0 836->850 838 hours-per-week <= 52.5 entropy = 0.592 samples = 14 value = [2, 12] class = 1 837->838 849 entropy = 0.0 samples = 4 value = [0, 4] class = 1 837->849 839 age <= 54.5 entropy = 0.684 samples = 11 value = [2, 9] class = 1 838->839 848 entropy = 0.0 samples = 3 value = [0, 3] class = 1 838->848 840 hours-per-week <= 44.0 entropy = 0.811 samples = 8 value = [2, 6] class = 1 839->840 847 entropy = 0.0 samples = 3 value = [0, 3] class = 1 839->847 841 entropy = 0.0 samples = 2 value = [0, 2] class = 1 840->841 842 race_White <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 840->842 843 entropy = 0.0 samples = 1 value = [0, 1] class = 1 842->843 844 sex_Male <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 842->844 845 entropy = 0.0 samples = 1 value = [1, 0] class = 0 844->845 846 entropy = 0.811 samples = 4 value = [1, 3] class = 1 844->846 851 age <= 53.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 850->851 854 entropy = 0.0 samples = 4 value = [4, 0] class = 0 850->854 852 entropy = 0.0 samples = 4 value = [0, 4] class = 1 851->852 853 entropy = 1.0 samples = 2 value = [1, 1] class = 0 851->853 857 race_White <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] class = 0 856->857 860 entropy = 0.0 samples = 2 value = [0, 2] class = 1 856->860 858 entropy = 0.0 samples = 1 value = [0, 1] class = 1 857->858 859 entropy = 0.0 samples = 10 value = [10, 0] class = 0 857->859 862 age <= 52.5 entropy = 0.883 samples = 63 value = [19, 44] class = 1 861->862 903 entropy = 0.0 samples = 3 value = [3, 0] class = 0 861->903 863 hours-per-week <= 46.5 entropy = 0.811 samples = 56 value = [14, 42] class = 1 862->863 900 age <= 59.0 entropy = 0.863 samples = 7 value = [5, 2] class = 0 862->900 864 sex_Male <= 0.5 entropy = 0.958 samples = 29 value = [11, 18] class = 1 863->864 887 sex_Female <= 0.5 entropy = 0.503 samples = 27 value = [3, 24] class = 1 863->887 865 age <= 41.0 entropy = 0.811 samples = 8 value = [6, 2] class = 0 864->865 870 age <= 32.0 entropy = 0.792 samples = 21 value = [5, 16] class = 1 864->870 866 entropy = 0.0 samples = 3 value = [3, 0] class = 0 865->866 867 workclass_Private <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 865->867 868 entropy = 0.811 samples = 4 value = [3, 1] class = 0 867->868 869 entropy = 0.0 samples = 1 value = [0, 1] class = 1 867->869 871 entropy = 0.0 samples = 1 value = [1, 0] class = 0 870->871 872 workclass_Public <= 0.5 entropy = 0.722 samples = 20 value = [4, 16] class = 1 870->872 873 workclass_Private <= 0.5 entropy = 0.523 samples = 17 value = [2, 15] class = 1 872->873 886 entropy = 0.918 samples = 3 value = [2, 1] class = 0 872->886 874 entropy = 0.0 samples = 5 value = [0, 5] class = 1 873->874 875 hours-per-week <= 38.0 entropy = 0.65 samples = 12 value = [2, 10] class = 1 873->875 876 entropy = 0.0 samples = 2 value = [0, 2] class = 1 875->876 877 hours-per-week <= 41.0 entropy = 0.722 samples = 10 value = [2, 8] class = 1 875->877 878 age <= 49.0 entropy = 0.811 samples = 8 value = [2, 6] class = 1 877->878 885 entropy = 0.0 samples = 2 value = [0, 2] class = 1 877->885 879 age <= 46.0 entropy = 0.918 samples = 6 value = [2, 4] class = 1 878->879 884 entropy = 0.0 samples = 2 value = [0, 2] class = 1 878->884 880 education <= 15.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 879->880 883 entropy = 0.0 samples = 1 value = [1, 0] class = 0 879->883 881 entropy = 0.0 samples = 3 value = [0, 3] class = 1 880->881 882 entropy = 1.0 samples = 2 value = [1, 1] class = 0 880->882 888 education <= 15.5 entropy = 0.75 samples = 14 value = [3, 11] class = 1 887->888 899 entropy = 0.0 samples = 13 value = [0, 13] class = 1 887->899 889 age <= 45.0 entropy = 0.881 samples = 10 value = [3, 7] class = 1 888->889 898 entropy = 0.0 samples = 4 value = [0, 4] class = 1 888->898 890 hours-per-week <= 57.5 entropy = 0.954 samples = 8 value = [3, 5] class = 1 889->890 897 entropy = 0.0 samples = 2 value = [0, 2] class = 1 889->897 891 age <= 36.0 entropy = 0.863 samples = 7 value = [2, 5] class = 1 890->891 896 entropy = 0.0 samples = 1 value = [1, 0] class = 0 890->896 892 entropy = 0.0 samples = 2 value = [0, 2] class = 1 891->892 893 hours-per-week <= 52.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 891->893 894 entropy = 0.918 samples = 3 value = [2, 1] class = 0 893->894 895 entropy = 0.0 samples = 2 value = [0, 2] class = 1 893->895 901 entropy = 0.0 samples = 4 value = [4, 0] class = 0 900->901 902 entropy = 0.918 samples = 3 value = [1, 2] class = 1 900->902 905 education <= 8.5 entropy = 0.887 samples = 3222 value = [2241, 981] class = 0 904->905 2522 hours-per-week <= 41.5 entropy = 0.907 samples = 1515 value = [489, 1026] class = 1 904->2522 906 hours-per-week <= 39.5 entropy = 0.491 samples = 551 value = [492, 59] class = 0 905->906 1073 age <= 29.5 entropy = 0.93 samples = 2671 value = [1749, 922] class = 0 905->1073 907 sex_Female <= 0.5 entropy = 0.178 samples = 112 value = [109, 3] class = 0 906->907 920 age <= 38.225 entropy = 0.551 samples = 439 value = [383, 56] class = 0 906->920 908 education <= 7.5 entropy = 0.242 samples = 75 value = [72, 3] class = 0 907->908 919 entropy = 0.0 samples = 37 value = [37, 0] class = 0 907->919 909 age <= 76.5 entropy = 0.183 samples = 72 value = [70, 2] class = 0 908->909 918 entropy = 0.918 samples = 3 value = [2, 1] class = 0 908->918 910 education <= 5.5 entropy = 0.111 samples = 68 value = [67, 1] class = 0 909->910 917 entropy = 0.811 samples = 4 value = [3, 1] class = 0 909->917 911 entropy = 0.0 samples = 44 value = [44, 0] class = 0 910->911 912 age <= 53.5 entropy = 0.25 samples = 24 value = [23, 1] class = 0 910->912 913 age <= 51.0 entropy = 0.439 samples = 11 value = [10, 1] class = 0 912->913 916 entropy = 0.0 samples = 13 value = [13, 0] class = 0 912->916 914 entropy = 0.0 samples = 10 value = [10, 0] class = 0 913->914 915 entropy = 0.0 samples = 1 value = [0, 1] class = 1 913->915 921 hours-per-week <= 77.5 entropy = 0.355 samples = 164 value = [153, 11] class = 0 920->921 960 workclass_Self-emp <= 0.5 entropy = 0.643 samples = 275 value = [230, 45] class = 0 920->960 922 workclass_Self-emp <= 0.5 entropy = 0.334 samples = 162 value = [152, 10] class = 0 921->922 959 entropy = 1.0 samples = 2 value = [1, 1] class = 0 921->959 923 age <= 28.5 entropy = 0.282 samples = 143 value = [136, 7] class = 0 922->923 952 age <= 29.0 entropy = 0.629 samples = 19 value = [16, 3] class = 0 922->952 924 entropy = 0.0 samples = 49 value = [49, 0] class = 0 923->924 925 age <= 33.5 entropy = 0.382 samples = 94 value = [87, 7] class = 0 923->925 926 education <= 5.5 entropy = 0.246 samples = 49 value = [47, 2] class = 0 925->926 937 education <= 4.5 entropy = 0.503 samples = 45 value = [40, 5] class = 0 925->937 927 entropy = 0.0 samples = 22 value = [22, 0] class = 0 926->927 928 age <= 31.5 entropy = 0.381 samples = 27 value = [25, 2] class = 0 926->928 929 education <= 7.5 entropy = 0.503 samples = 18 value = [16, 2] class = 0 928->929 936 entropy = 0.0 samples = 9 value = [9, 0] class = 0 928->936 930 age <= 30.5 entropy = 0.353 samples = 15 value = [14, 1] class = 0 929->930 935 entropy = 0.918 samples = 3 value = [2, 1] class = 0 929->935 931 entropy = 0.0 samples = 10 value = [10, 0] class = 0 930->931 932 education <= 6.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 930->932 933 entropy = 0.918 samples = 3 value = [2, 1] class = 0 932->933 934 entropy = 0.0 samples = 2 value = [2, 0] class = 0 932->934 938 sex_Male <= 0.5 entropy = 0.75 samples = 14 value = [11, 3] class = 0 937->938 947 hours-per-week <= 43.5 entropy = 0.345 samples = 31 value = [29, 2] class = 0 937->947 939 entropy = 0.0 samples = 1 value = [0, 1] class = 1 938->939 940 age <= 36.5 entropy = 0.619 samples = 13 value = [11, 2] class = 0 938->940 941 age <= 34.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 940->941 946 entropy = 0.0 samples = 6 value = [6, 0] class = 0 940->946 942 entropy = 0.0 samples = 1 value = [1, 0] class = 0 941->942 943 age <= 35.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 941->943 944 entropy = 0.811 samples = 4 value = [3, 1] class = 0 943->944 945 entropy = 1.0 samples = 2 value = [1, 1] class = 0 943->945 948 entropy = 0.0 samples = 17 value = [17, 0] class = 0 947->948 949 hours-per-week <= 45.5 entropy = 0.592 samples = 14 value = [12, 2] class = 0 947->949 950 entropy = 1.0 samples = 4 value = [2, 2] class = 0 949->950 951 entropy = 0.0 samples = 10 value = [10, 0] class = 0 949->951 953 age <= 27.5 entropy = 0.918 samples = 9 value = [6, 3] class = 0 952->953 958 entropy = 0.0 samples = 10 value = [10, 0] class = 0 952->958 954 age <= 25.0 entropy = 0.811 samples = 8 value = [6, 2] class = 0 953->954 957 entropy = 0.0 samples = 1 value = [0, 1] class = 1 953->957 955 entropy = 1.0 samples = 4 value = [2, 2] class = 0 954->955 956 entropy = 0.0 samples = 4 value = [4, 0] class = 0 954->956 961 age <= 41.5 entropy = 0.588 samples = 226 value = [194, 32] class = 0 960->961 1050 hours-per-week <= 59.5 entropy = 0.835 samples = 49 value = [36, 13] class = 0 960->1050 962 education <= 3.5 entropy = 0.834 samples = 34 value = [25, 9] class = 0 961->962 983 education <= 4.5 entropy = 0.529 samples = 192 value = [169, 23] class = 0 961->983 963 age <= 40.5 entropy = 0.469 samples = 10 value = [9, 1] class = 0 962->963 968 age <= 40.5 entropy = 0.918 samples = 24 value = [16, 8] class = 0 962->968 964 entropy = 0.0 samples = 4 value = [4, 0] class = 0 963->964 965 education <= 2.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 963->965 966 entropy = 0.918 samples = 3 value = [2, 1] class = 0 965->966 967 entropy = 0.0 samples = 3 value = [3, 0] class = 0 965->967 969 education <= 6.5 entropy = 0.811 samples = 20 value = [15, 5] class = 0 968->969 982 entropy = 0.811 samples = 4 value = [1, 3] class = 1 968->982 970 sex_Female <= 0.5 entropy = 0.98 samples = 12 value = [7, 5] class = 0 969->970 981 entropy = 0.0 samples = 8 value = [8, 0] class = 0 969->981 971 race_Black <= 0.5 entropy = 0.946 samples = 11 value = [7, 4] class = 0 970->971 980 entropy = 0.0 samples = 1 value = [0, 1] class = 1 970->980 972 hours-per-week <= 51.5 entropy = 0.881 samples = 10 value = [7, 3] class = 0 971->972 979 entropy = 0.0 samples = 1 value = [0, 1] class = 1 971->979 973 age <= 39.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 972->973 978 entropy = 0.0 samples = 1 value = [0, 1] class = 1 972->978 974 entropy = 0.0 samples = 4 value = [4, 0] class = 0 973->974 975 hours-per-week <= 41.0 entropy = 0.971 samples = 5 value = [3, 2] class = 0 973->975 976 entropy = 1.0 samples = 4 value = [2, 2] class = 0 975->976 977 entropy = 0.0 samples = 1 value = [1, 0] class = 0 975->977 984 hours-per-week <= 49.5 entropy = 0.316 samples = 70 value = [66, 4] class = 0 983->984 997 education <= 6.5 entropy = 0.624 samples = 122 value = [103, 19] class = 0 983->997 985 race_Asian <= 0.5 entropy = 0.127 samples = 57 value = [56, 1] class = 0 984->985 988 education <= 3.5 entropy = 0.779 samples = 13 value = [10, 3] class = 0 984->988 986 entropy = 0.0 samples = 54 value = [54, 0] class = 0 985->986 987 entropy = 0.918 samples = 3 value = [2, 1] class = 0 985->987 989 entropy = 0.0 samples = 5 value = [5, 0] class = 0 988->989 990 age <= 47.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 988->990 991 entropy = 0.0 samples = 1 value = [0, 1] class = 1 990->991 992 age <= 52.0 entropy = 0.863 samples = 7 value = [5, 2] class = 0 990->992 993 entropy = 0.0 samples = 2 value = [2, 0] class = 0 992->993 994 age <= 54.0 entropy = 0.971 samples = 5 value = [3, 2] class = 0 992->994 995 entropy = 0.0 samples = 1 value = [0, 1] class = 1 994->995 996 entropy = 0.811 samples = 4 value = [3, 1] class = 0 994->996 998 age <= 63.5 entropy = 0.748 samples = 75 value = [59, 16] class = 0 997->998 1039 age <= 49.5 entropy = 0.342 samples = 47 value = [44, 3] class = 0 997->1039 999 age <= 59.5 entropy = 0.698 samples = 69 value = [56, 13] class = 0 998->999 1034 age <= 73.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 998->1034 1000 workclass_Public <= 0.5 entropy = 0.775 samples = 57 value = [44, 13] class = 0 999->1000 1033 entropy = 0.0 samples = 12 value = [12, 0] class = 0 999->1033 1001 sex_Female <= 0.5 entropy = 0.827 samples = 50 value = [37, 13] class = 0 1000->1001 1032 entropy = 0.0 samples = 7 value = [7, 0] class = 0 1000->1032 1002 age <= 57.5 entropy = 0.876 samples = 44 value = [31, 13] class = 0 1001->1002 1031 entropy = 0.0 samples = 6 value = [6, 0] class = 0 1001->1031 1003 race_Black <= 0.5 entropy = 0.79 samples = 38 value = [29, 9] class = 0 1002->1003 1026 hours-per-week <= 55.0 entropy = 0.918 samples = 6 value = [2, 4] class = 1 1002->1026 1004 age <= 54.5 entropy = 0.684 samples = 33 value = [27, 6] class = 0 1003->1004 1023 age <= 55.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1003->1023 1005 age <= 50.0 entropy = 0.764 samples = 27 value = [21, 6] class = 0 1004->1005 1022 entropy = 0.0 samples = 6 value = [6, 0] class = 0 1004->1022 1006 education <= 5.5 entropy = 0.503 samples = 18 value = [16, 2] class = 0 1005->1006 1015 age <= 53.5 entropy = 0.991 samples = 9 value = [5, 4] class = 0 1005->1015 1007 entropy = 0.0 samples = 7 value = [7, 0] class = 0 1006->1007 1008 hours-per-week <= 41.0 entropy = 0.684 samples = 11 value = [9, 2] class = 0 1006->1008 1009 age <= 43.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 1008->1009 1014 entropy = 0.0 samples = 5 value = [5, 0] class = 0 1008->1014 1010 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1009->1010 1011 age <= 44.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1009->1011 1012 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1011->1012 1013 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1011->1013 1016 age <= 52.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 1015->1016 1021 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1015->1021 1017 hours-per-week <= 46.0 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1016->1017 1020 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1016->1020 1018 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1017->1018 1019 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1017->1019 1024 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1023->1024 1025 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1023->1025 1027 hours-per-week <= 45.0 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1026->1027 1030 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1026->1030 1028 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1027->1028 1029 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1027->1029 1035 sex_Female <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1034->1035 1038 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1034->1038 1036 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1035->1036 1037 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1035->1037 1040 entropy = 0.0 samples = 20 value = [20, 0] class = 0 1039->1040 1041 workclass_Private <= 0.5 entropy = 0.503 samples = 27 value = [24, 3] class = 0 1039->1041 1042 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1041->1042 1043 hours-per-week <= 61.0 entropy = 0.391 samples = 26 value = [24, 2] class = 0 1041->1043 1044 age <= 59.5 entropy = 0.242 samples = 25 value = [24, 1] class = 0 1043->1044 1049 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1043->1049 1045 entropy = 0.0 samples = 16 value = [16, 0] class = 0 1044->1045 1046 age <= 61.5 entropy = 0.503 samples = 9 value = [8, 1] class = 0 1044->1046 1047 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1046->1047 1048 entropy = 0.0 samples = 7 value = [7, 0] class = 0 1046->1048 1051 education <= 6.5 entropy = 0.952 samples = 35 value = [22, 13] class = 0 1050->1051 1072 entropy = 0.0 samples = 14 value = [14, 0] class = 0 1050->1072 1052 hours-per-week <= 49.0 entropy = 0.869 samples = 31 value = [22, 9] class = 0 1051->1052 1071 entropy = 0.0 samples = 4 value = [0, 4] class = 1 1051->1071 1053 education <= 4.5 entropy = 0.667 samples = 23 value = [19, 4] class = 0 1052->1053 1064 age <= 64.0 entropy = 0.954 samples = 8 value = [3, 5] class = 1 1052->1064 1054 age <= 44.5 entropy = 0.918 samples = 12 value = [8, 4] class = 0 1053->1054 1063 entropy = 0.0 samples = 11 value = [11, 0] class = 0 1053->1063 1055 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1054->1055 1056 education <= 3.5 entropy = 0.991 samples = 9 value = [5, 4] class = 0 1054->1056 1057 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1056->1057 1058 age <= 53.0 entropy = 0.863 samples = 7 value = [5, 2] class = 0 1056->1058 1059 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1058->1059 1060 age <= 56.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1058->1060 1061 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1060->1061 1062 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1060->1062 1065 age <= 55.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 1064->1065 1070 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1064->1070 1066 hours-per-week <= 52.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1065->1066 1069 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1065->1069 1067 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1066->1067 1068 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1066->1068 1074 age <= 23.5 entropy = 0.59 samples = 345 value = [296, 49] class = 0 1073->1074 1201 hours-per-week <= 34.5 entropy = 0.955 samples = 2326 value = [1453, 873] class = 0 1073->1201 1075 entropy = 0.0 samples = 68 value = [68, 0] class = 0 1074->1075 1076 hours-per-week <= 64.0 entropy = 0.673 samples = 277 value = [228, 49] class = 0 1074->1076 1077 age <= 28.5 entropy = 0.643 samples = 263 value = [220, 43] class = 0 1076->1077 1192 age <= 25.5 entropy = 0.985 samples = 14 value = [8, 6] class = 0 1076->1192 1078 race_Black <= 0.5 entropy = 0.588 samples = 198 value = [170, 28] class = 0 1077->1078 1155 race_Asian <= 0.5 entropy = 0.779 samples = 65 value = [50, 15] class = 0 1077->1155 1079 hours-per-week <= 41.0 entropy = 0.615 samples = 184 value = [156, 28] class = 0 1078->1079 1154 entropy = 0.0 samples = 14 value = [14, 0] class = 0 1078->1154 1080 race_Asian <= 0.5 entropy = 0.52 samples = 120 value = [106, 14] class = 0 1079->1080 1129 hours-per-week <= 44.5 entropy = 0.758 samples = 64 value = [50, 14] class = 0 1079->1129 1081 workclass_Public <= 0.5 entropy = 0.483 samples = 115 value = [103, 12] class = 0 1080->1081 1126 sex_Male <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1080->1126 1082 education <= 9.5 entropy = 0.51 samples = 106 value = [94, 12] class = 0 1081->1082 1125 entropy = 0.0 samples = 9 value = [9, 0] class = 0 1081->1125 1083 age <= 24.5 entropy = 0.404 samples = 62 value = [57, 5] class = 0 1082->1083 1104 hours-per-week <= 35.5 entropy = 0.632 samples = 44 value = [37, 7] class = 0 1082->1104 1084 entropy = 0.0 samples = 10 value = [10, 0] class = 0 1083->1084 1085 hours-per-week <= 28.0 entropy = 0.457 samples = 52 value = [47, 5] class = 0 1083->1085 1086 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1085->1086 1087 workclass_Private <= 0.5 entropy = 0.408 samples = 49 value = [45, 4] class = 0 1085->1087 1088 age <= 26.0 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1087->1088 1091 age <= 25.5 entropy = 0.359 samples = 44 value = [41, 3] class = 0 1087->1091 1089 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1088->1089 1090 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1088->1090 1092 entropy = 0.0 samples = 11 value = [11, 0] class = 0 1091->1092 1093 sex_Male <= 0.5 entropy = 0.439 samples = 33 value = [30, 3] class = 0 1091->1093 1094 age <= 26.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1093->1094 1097 age <= 26.5 entropy = 0.371 samples = 28 value = [26, 2] class = 0 1093->1097 1095 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1094->1095 1096 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1094->1096 1098 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1097->1098 1099 hours-per-week <= 37.5 entropy = 0.414 samples = 24 value = [22, 2] class = 0 1097->1099 1100 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1099->1100 1101 age <= 27.5 entropy = 0.426 samples = 23 value = [21, 2] class = 0 1099->1101 1102 entropy = 0.503 samples = 9 value = [8, 1] class = 0 1101->1102 1103 entropy = 0.371 samples = 14 value = [13, 1] class = 0 1101->1103 1105 entropy = 0.0 samples = 8 value = [8, 0] class = 0 1104->1105 1106 hours-per-week <= 37.0 entropy = 0.711 samples = 36 value = [29, 7] class = 0 1104->1106 1107 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1106->1107 1108 age <= 26.5 entropy = 0.661 samples = 35 value = [29, 6] class = 0 1106->1108 1109 sex_Female <= 0.5 entropy = 0.469 samples = 20 value = [18, 2] class = 0 1108->1109 1116 education <= 10.5 entropy = 0.837 samples = 15 value = [11, 4] class = 0 1108->1116 1110 age <= 24.5 entropy = 0.523 samples = 17 value = [15, 2] class = 0 1109->1110 1115 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1109->1115 1111 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1110->1111 1112 age <= 25.5 entropy = 0.391 samples = 13 value = [12, 1] class = 0 1110->1112 1113 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1112->1113 1114 entropy = 0.503 samples = 9 value = [8, 1] class = 0 1112->1114 1117 age <= 27.5 entropy = 0.918 samples = 12 value = [8, 4] class = 0 1116->1117 1124 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1116->1124 1118 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1117->1118 1119 sex_Female <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 1117->1119 1120 workclass_Private <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 1119->1120 1123 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1119->1123 1121 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1120->1121 1122 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1120->1122 1127 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1126->1127 1128 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1126->1128 1130 workclass_Private <= 0.5 entropy = 0.985 samples = 7 value = [3, 4] class = 1 1129->1130 1135 hours-per-week <= 51.5 entropy = 0.67 samples = 57 value = [47, 10] class = 0 1129->1135 1131 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1130->1131 1132 age <= 25.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 1130->1132 1133 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1132->1133 1134 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1132->1134 1136 hours-per-week <= 47.0 entropy = 0.769 samples = 40 value = [31, 9] class = 0 1135->1136 1151 age <= 24.5 entropy = 0.323 samples = 17 value = [16, 1] class = 0 1135->1151 1137 age <= 24.5 entropy = 0.353 samples = 15 value = [14, 1] class = 0 1136->1137 1140 sex_Male <= 0.5 entropy = 0.904 samples = 25 value = [17, 8] class = 0 1136->1140 1138 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1137->1138 1139 entropy = 0.0 samples = 12 value = [12, 0] class = 0 1137->1139 1141 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1140->1141 1142 age <= 27.5 entropy = 0.828 samples = 23 value = [17, 6] class = 0 1140->1142 1143 age <= 26.5 entropy = 0.523 samples = 17 value = [15, 2] class = 0 1142->1143 1148 hours-per-week <= 49.0 entropy = 0.918 samples = 6 value = [2, 4] class = 1 1142->1148 1144 age <= 25.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 1143->1144 1147 entropy = 0.0 samples = 8 value = [8, 0] class = 0 1143->1147 1145 entropy = 0.0 samples = 5 value = [5, 0] class = 0 1144->1145 1146 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1144->1146 1149 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1148->1149 1150 entropy = 0.0 samples = 4 value = [0, 4] class = 1 1148->1150 1152 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1151->1152 1153 entropy = 0.0 samples = 15 value = [15, 0] class = 0 1151->1153 1156 workclass_Private <= 0.5 entropy = 0.798 samples = 62 value = [47, 15] class = 0 1155->1156 1191 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1155->1191 1157 race_White <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1156->1157 1164 hours-per-week <= 39.0 entropy = 0.757 samples = 55 value = [43, 12] class = 0 1156->1164 1158 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1157->1158 1159 sex_Male <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 1157->1159 1160 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1159->1160 1161 hours-per-week <= 47.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1159->1161 1162 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1161->1162 1163 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1161->1163 1165 entropy = 0.0 samples = 5 value = [5, 0] class = 0 1164->1165 1166 sex_Male <= 0.5 entropy = 0.795 samples = 50 value = [38, 12] class = 0 1164->1166 1167 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1166->1167 1168 education <= 10.5 entropy = 0.755 samples = 46 value = [36, 10] class = 0 1166->1168 1169 hours-per-week <= 57.5 entropy = 0.79 samples = 38 value = [29, 9] class = 0 1168->1169 1188 hours-per-week <= 44.0 entropy = 0.544 samples = 8 value = [7, 1] class = 0 1168->1188 1170 race_Hispanic <= 0.5 entropy = 0.8 samples = 37 value = [28, 9] class = 0 1169->1170 1187 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1169->1187 1171 hours-per-week <= 49.0 entropy = 0.811 samples = 36 value = [27, 9] class = 0 1170->1171 1186 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1170->1186 1172 education <= 9.5 entropy = 0.771 samples = 31 value = [24, 7] class = 0 1171->1172 1183 education <= 9.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1171->1183 1173 hours-per-week <= 42.5 entropy = 0.65 samples = 24 value = [20, 4] class = 0 1172->1173 1178 race_Black <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1172->1178 1174 race_White <= 0.5 entropy = 0.722 samples = 20 value = [16, 4] class = 0 1173->1174 1177 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1173->1177 1175 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1174->1175 1176 entropy = 0.764 samples = 18 value = [14, 4] class = 0 1174->1176 1179 hours-per-week <= 42.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 1178->1179 1182 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1178->1182 1180 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1179->1180 1181 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1179->1181 1184 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1183->1184 1185 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1183->1185 1189 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1188->1189 1190 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1188->1190 1193 education <= 10.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 1192->1193 1196 race_Black <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 1192->1196 1194 entropy = 0.0 samples = 4 value = [0, 4] class = 1 1193->1194 1195 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1193->1195 1197 workclass_Private <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 1196->1197 1200 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1196->1200 1198 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1197->1198 1199 entropy = 0.0 samples = 6 value = [6, 0] class = 0 1197->1199 1202 education <= 9.5 entropy = 0.55 samples = 228 value = [199, 29] class = 0 1201->1202 1277 age <= 35.5 entropy = 0.972 samples = 2098 value = [1254, 844] class = 0 1201->1277 1203 hours-per-week <= 15.5 entropy = 0.336 samples = 145 value = [136, 9] class = 0 1202->1203 1238 age <= 61.5 entropy = 0.797 samples = 83 value = [63, 20] class = 0 1202->1238 1204 entropy = 0.0 samples = 26 value = [26, 0] class = 0 1203->1204 1205 sex_Female <= 0.5 entropy = 0.387 samples = 119 value = [110, 9] class = 0 1203->1205 1206 age <= 57.5 entropy = 0.206 samples = 62 value = [60, 2] class = 0 1205->1206 1213 race_White <= 0.5 entropy = 0.537 samples = 57 value = [50, 7] class = 0 1205->1213 1207 entropy = 0.0 samples = 31 value = [31, 0] class = 0 1206->1207 1208 age <= 59.5 entropy = 0.345 samples = 31 value = [29, 2] class = 0 1206->1208 1209 hours-per-week <= 24.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1208->1209 1212 entropy = 0.0 samples = 26 value = [26, 0] class = 0 1208->1212 1210 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1209->1210 1211 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1209->1211 1214 entropy = 0.0 samples = 10 value = [10, 0] class = 0 1213->1214 1215 hours-per-week <= 21.0 entropy = 0.607 samples = 47 value = [40, 7] class = 0 1213->1215 1216 workclass_Self-emp <= 0.5 entropy = 0.831 samples = 19 value = [14, 5] class = 0 1215->1216 1229 hours-per-week <= 28.5 entropy = 0.371 samples = 28 value = [26, 2] class = 0 1215->1229 1217 age <= 47.5 entropy = 0.65 samples = 12 value = [10, 2] class = 0 1216->1217 1224 age <= 44.0 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1216->1224 1218 age <= 44.0 entropy = 0.863 samples = 7 value = [5, 2] class = 0 1217->1218 1223 entropy = 0.0 samples = 5 value = [5, 0] class = 0 1217->1223 1219 hours-per-week <= 18.0 entropy = 0.65 samples = 6 value = [5, 1] class = 0 1218->1219 1222 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1218->1222 1220 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1219->1220 1221 entropy = 0.0 samples = 5 value = [5, 0] class = 0 1219->1221 1225 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1224->1225 1226 age <= 62.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1224->1226 1227 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1226->1227 1228 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1226->1228 1230 entropy = 0.0 samples = 14 value = [14, 0] class = 0 1229->1230 1231 hours-per-week <= 31.0 entropy = 0.592 samples = 14 value = [12, 2] class = 0 1229->1231 1232 age <= 38.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 1231->1232 1237 entropy = 0.0 samples = 5 value = [5, 0] class = 0 1231->1237 1233 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1232->1233 1234 age <= 54.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 1232->1234 1235 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1234->1235 1236 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1234->1236 1239 race_White <= 0.5 entropy = 0.905 samples = 53 value = [36, 17] class = 0 1238->1239 1268 workclass_Self-emp <= 0.5 entropy = 0.469 samples = 30 value = [27, 3] class = 0 1238->1268 1240 entropy = 0.0 samples = 6 value = [6, 0] class = 0 1239->1240 1241 workclass_Private <= 0.5 entropy = 0.944 samples = 47 value = [30, 17] class = 0 1239->1241 1242 hours-per-week <= 22.0 entropy = 0.65 samples = 18 value = [15, 3] class = 0 1241->1242 1251 age <= 32.5 entropy = 0.999 samples = 29 value = [15, 14] class = 0 1241->1251 1243 age <= 33.5 entropy = 0.918 samples = 9 value = [6, 3] class = 0 1242->1243 1250 entropy = 0.0 samples = 9 value = [9, 0] class = 0 1242->1250 1244 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1243->1244 1245 age <= 57.0 entropy = 0.811 samples = 8 value = [6, 2] class = 0 1243->1245 1246 age <= 38.0 entropy = 0.592 samples = 7 value = [6, 1] class = 0 1245->1246 1249 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1245->1249 1247 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1246->1247 1248 entropy = 0.0 samples = 5 value = [5, 0] class = 0 1246->1248 1252 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1251->1252 1253 hours-per-week <= 33.0 entropy = 0.99 samples = 25 value = [11, 14] class = 1 1251->1253 1254 hours-per-week <= 31.0 entropy = 0.98 samples = 24 value = [10, 14] class = 1 1253->1254 1267 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1253->1267 1255 hours-per-week <= 27.5 entropy = 0.994 samples = 22 value = [10, 12] class = 1 1254->1255 1266 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1254->1266 1256 hours-per-week <= 17.0 entropy = 0.971 samples = 20 value = [8, 12] class = 1 1255->1256 1265 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1255->1265 1257 age <= 43.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 1256->1257 1260 age <= 53.0 entropy = 0.811 samples = 12 value = [3, 9] class = 1 1256->1260 1258 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1257->1258 1259 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1257->1259 1261 age <= 38.725 entropy = 0.544 samples = 8 value = [1, 7] class = 1 1260->1261 1264 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1260->1264 1262 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1261->1262 1263 entropy = 0.0 samples = 6 value = [0, 6] class = 1 1261->1263 1269 entropy = 0.0 samples = 19 value = [19, 0] class = 0 1268->1269 1270 age <= 62.5 entropy = 0.845 samples = 11 value = [8, 3] class = 0 1268->1270 1271 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1270->1271 1272 hours-per-week <= 12.5 entropy = 0.918 samples = 9 value = [6, 3] class = 0 1270->1272 1273 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1272->1273 1274 age <= 71.0 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1272->1274 1275 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1274->1275 1276 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1274->1276 1278 hours-per-week <= 47.0 entropy = 0.894 samples = 431 value = [297, 134] class = 0 1277->1278 1519 age <= 62.5 entropy = 0.984 samples = 1667 value = [957, 710] class = 0 1277->1519 1279 race_White <= 0.5 entropy = 0.808 samples = 282 value = [212, 70] class = 0 1278->1279 1400 sex_Female <= 0.5 entropy = 0.986 samples = 149 value = [85, 64] class = 0 1278->1400 1280 workclass_Private <= 0.5 entropy = 0.384 samples = 40 value = [37, 3] class = 0 1279->1280 1289 sex_Male <= 0.5 entropy = 0.851 samples = 242 value = [175, 67] class = 0 1279->1289 1281 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1280->1281 1282 race_Amer-Indian <= 0.5 entropy = 0.303 samples = 37 value = [35, 2] class = 0 1280->1282 1283 education <= 9.5 entropy = 0.191 samples = 34 value = [33, 1] class = 0 1282->1283 1288 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1282->1288 1284 entropy = 0.0 samples = 24 value = [24, 0] class = 0 1283->1284 1285 age <= 33.5 entropy = 0.469 samples = 10 value = [9, 1] class = 0 1283->1285 1286 entropy = 0.0 samples = 8 value = [8, 0] class = 0 1285->1286 1287 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1285->1287 1290 hours-per-week <= 43.0 entropy = 0.958 samples = 29 value = [18, 11] class = 0 1289->1290 1313 education <= 10.5 entropy = 0.831 samples = 213 value = [157, 56] class = 0 1289->1313 1291 education <= 10.5 entropy = 0.983 samples = 26 value = [15, 11] class = 0 1290->1291 1312 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1290->1312 1292 age <= 30.5 entropy = 0.932 samples = 23 value = [15, 8] class = 0 1291->1292 1311 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1291->1311 1293 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1292->1293 1294 workclass_Public <= 0.5 entropy = 0.971 samples = 20 value = [12, 8] class = 0 1292->1294 1295 age <= 32.5 entropy = 0.949 samples = 19 value = [12, 7] class = 0 1294->1295 1310 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1294->1310 1296 age <= 31.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 1295->1296 1299 age <= 33.5 entropy = 1.0 samples = 12 value = [6, 6] class = 0 1295->1299 1297 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1296->1297 1298 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1296->1298 1300 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1299->1300 1301 workclass_Private <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 1299->1301 1302 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1301->1302 1303 education <= 9.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1301->1303 1304 hours-per-week <= 39.0 entropy = 0.918 samples = 6 value = [4, 2] class = 0 1303->1304 1309 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1303->1309 1305 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1304->1305 1306 age <= 34.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1304->1306 1307 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1306->1307 1308 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1306->1308 1314 hours-per-week <= 45.5 entropy = 0.849 samples = 196 value = [142, 54] class = 0 1313->1314 1389 age <= 30.5 entropy = 0.523 samples = 17 value = [15, 2] class = 0 1313->1389 1315 hours-per-week <= 37.0 entropy = 0.853 samples = 194 value = [140, 54] class = 0 1314->1315 1388 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1314->1388 1316 age <= 33.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 1315->1316 1319 age <= 33.5 entropy = 0.862 samples = 186 value = [133, 53] class = 0 1315->1319 1317 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1316->1317 1318 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1316->1318 1320 hours-per-week <= 38.5 entropy = 0.894 samples = 119 value = [82, 37] class = 0 1319->1320 1361 workclass_Private <= 0.5 entropy = 0.793 samples = 67 value = [51, 16] class = 0 1319->1361 1321 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1320->1321 1322 workclass_Public <= 0.5 entropy = 0.887 samples = 118 value = [82, 36] class = 0 1320->1322 1323 hours-per-week <= 39.5 entropy = 0.908 samples = 105 value = [71, 34] class = 0 1322->1323 1356 age <= 32.5 entropy = 0.619 samples = 13 value = [11, 2] class = 0 1322->1356 1324 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1323->1324 1325 hours-per-week <= 40.5 entropy = 0.912 samples = 104 value = [70, 34] class = 0 1323->1325 1326 age <= 30.5 entropy = 0.899 samples = 92 value = [63, 29] class = 0 1325->1326 1349 hours-per-week <= 42.0 entropy = 0.98 samples = 12 value = [7, 5] class = 0 1325->1349 1327 education <= 9.5 entropy = 0.855 samples = 25 value = [18, 7] class = 0 1326->1327 1332 education <= 9.5 entropy = 0.913 samples = 67 value = [45, 22] class = 0 1326->1332 1328 entropy = 0.764 samples = 18 value = [14, 4] class = 0 1327->1328 1329 workclass_Private <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1327->1329 1330 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1329->1330 1331 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1329->1331 1333 workclass_Self-emp <= 0.5 entropy = 0.925 samples = 47 value = [31, 16] class = 0 1332->1333 1342 workclass_Self-emp <= 0.5 entropy = 0.881 samples = 20 value = [14, 6] class = 0 1332->1342 1334 age <= 32.5 entropy = 0.91 samples = 40 value = [27, 13] class = 0 1333->1334 1339 age <= 31.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1333->1339 1335 age <= 31.5 entropy = 0.918 samples = 27 value = [18, 9] class = 0 1334->1335 1338 entropy = 0.89 samples = 13 value = [9, 4] class = 0 1334->1338 1336 entropy = 0.918 samples = 12 value = [8, 4] class = 0 1335->1336 1337 entropy = 0.918 samples = 15 value = [10, 5] class = 0 1335->1337 1340 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1339->1340 1341 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1339->1341 1343 age <= 31.5 entropy = 0.937 samples = 17 value = [11, 6] class = 0 1342->1343 1348 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1342->1348 1344 entropy = 0.881 samples = 10 value = [7, 3] class = 0 1343->1344 1345 age <= 32.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1343->1345 1346 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1345->1346 1347 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1345->1347 1350 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1349->1350 1351 education <= 9.5 entropy = 0.946 samples = 11 value = [7, 4] class = 0 1349->1351 1352 age <= 32.0 entropy = 0.985 samples = 7 value = [3, 4] class = 1 1351->1352 1355 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1351->1355 1353 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1352->1353 1354 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1352->1354 1357 age <= 30.5 entropy = 0.439 samples = 11 value = [10, 1] class = 0 1356->1357 1360 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1356->1360 1358 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1357->1358 1359 entropy = 0.0 samples = 8 value = [8, 0] class = 0 1357->1359 1362 hours-per-week <= 42.5 entropy = 0.954 samples = 16 value = [10, 6] class = 0 1361->1362 1373 education <= 9.5 entropy = 0.714 samples = 51 value = [41, 10] class = 0 1361->1373 1363 education <= 9.5 entropy = 0.985 samples = 14 value = [8, 6] class = 0 1362->1363 1372 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1362->1372 1364 age <= 34.5 entropy = 0.991 samples = 9 value = [4, 5] class = 1 1363->1364 1369 age <= 34.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1363->1369 1365 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1364->1365 1366 workclass_Public <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 1364->1366 1367 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1366->1367 1368 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1366->1368 1370 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1369->1370 1371 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1369->1371 1374 hours-per-week <= 43.5 entropy = 0.614 samples = 33 value = [28, 5] class = 0 1373->1374 1383 age <= 34.5 entropy = 0.852 samples = 18 value = [13, 5] class = 0 1373->1383 1375 age <= 34.5 entropy = 0.491 samples = 28 value = [25, 3] class = 0 1374->1375 1380 hours-per-week <= 44.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1374->1380 1376 entropy = 0.0 samples = 14 value = [14, 0] class = 0 1375->1376 1377 hours-per-week <= 41.5 entropy = 0.75 samples = 14 value = [11, 3] class = 0 1375->1377 1378 entropy = 0.779 samples = 13 value = [10, 3] class = 0 1377->1378 1379 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1377->1379 1381 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1380->1381 1382 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1380->1382 1384 entropy = 1.0 samples = 8 value = [4, 4] class = 0 1383->1384 1385 hours-per-week <= 42.5 entropy = 0.469 samples = 10 value = [9, 1] class = 0 1383->1385 1386 entropy = 0.503 samples = 9 value = [8, 1] class = 0 1385->1386 1387 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1385->1387 1390 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1389->1390 1391 hours-per-week <= 41.0 entropy = 0.619 samples = 13 value = [11, 2] class = 0 1389->1391 1392 age <= 34.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 1391->1392 1399 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1391->1399 1393 workclass_Public <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 1392->1393 1398 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1392->1398 1394 age <= 31.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 1393->1394 1397 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1393->1397 1395 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1394->1395 1396 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1394->1396 1401 age <= 32.5 entropy = 0.992 samples = 141 value = [78, 63] class = 0 1400->1401 1516 age <= 30.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 1400->1516 1402 hours-per-week <= 54.5 entropy = 0.954 samples = 64 value = [40, 24] class = 0 1401->1402 1455 hours-per-week <= 49.0 entropy = 1.0 samples = 77 value = [38, 39] class = 1 1401->1455 1403 workclass_Private <= 0.5 entropy = 0.758 samples = 32 value = [25, 7] class = 0 1402->1403 1428 race_Hispanic <= 0.5 entropy = 0.997 samples = 32 value = [15, 17] class = 1 1402->1428 1404 entropy = 0.0 samples = 6 value = [6, 0] class = 0 1403->1404 1405 hours-per-week <= 51.5 entropy = 0.84 samples = 26 value = [19, 7] class = 0 1403->1405 1406 race_Asian <= 0.5 entropy = 0.887 samples = 23 value = [16, 7] class = 0 1405->1406 1427 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1405->1427 1407 education <= 10.5 entropy = 0.902 samples = 22 value = [15, 7] class = 0 1406->1407 1426 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1406->1426 1408 education <= 9.5 entropy = 0.918 samples = 21 value = [14, 7] class = 0 1407->1408 1425 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1407->1425 1409 hours-per-week <= 48.5 entropy = 0.811 samples = 12 value = [9, 3] class = 0 1408->1409 1418 hours-per-week <= 49.0 entropy = 0.991 samples = 9 value = [5, 4] class = 0 1408->1418 1410 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1409->1410 1411 hours-per-week <= 49.5 entropy = 0.722 samples = 10 value = [8, 2] class = 0 1409->1411 1412 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1411->1412 1413 age <= 30.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 1411->1413 1414 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1413->1414 1415 age <= 31.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 1413->1415 1416 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1415->1416 1417 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1415->1417 1419 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1418->1419 1420 age <= 30.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 1418->1420 1421 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1420->1421 1422 age <= 31.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1420->1422 1423 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1422->1423 1424 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1422->1424 1429 workclass_Private <= 0.5 entropy = 0.993 samples = 31 value = [14, 17] class = 1 1428->1429 1454 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1428->1454 1430 hours-per-week <= 62.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 1429->1430 1433 hours-per-week <= 85.0 entropy = 0.999 samples = 25 value = [13, 12] class = 0 1429->1433 1431 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1430->1431 1432 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1430->1432 1434 hours-per-week <= 76.0 entropy = 1.0 samples = 24 value = [12, 12] class = 0 1433->1434 1453 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1433->1453 1435 hours-per-week <= 68.5 entropy = 0.999 samples = 23 value = [12, 11] class = 0 1434->1435 1452 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1434->1452 1436 age <= 31.5 entropy = 1.0 samples = 22 value = [11, 11] class = 0 1435->1436 1451 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1435->1451 1437 hours-per-week <= 55.5 entropy = 0.985 samples = 14 value = [8, 6] class = 0 1436->1437 1446 education <= 10.5 entropy = 0.954 samples = 8 value = [3, 5] class = 1 1436->1446 1438 age <= 30.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1437->1438 1441 age <= 30.5 entropy = 0.991 samples = 9 value = [4, 5] class = 1 1437->1441 1439 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1438->1439 1440 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1438->1440 1442 education <= 9.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 1441->1442 1445 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1441->1445 1443 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1442->1443 1444 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1442->1444 1447 education <= 9.5 entropy = 0.985 samples = 7 value = [3, 4] class = 1 1446->1447 1450 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1446->1450 1448 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1447->1448 1449 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1447->1449 1456 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1455->1456 1457 workclass_Private <= 0.5 entropy = 0.999 samples = 74 value = [38, 36] class = 0 1455->1457 1458 race_Black <= 0.5 entropy = 0.958 samples = 29 value = [18, 11] class = 0 1457->1458 1487 hours-per-week <= 65.0 entropy = 0.991 samples = 45 value = [20, 25] class = 1 1457->1487 1459 workclass_Self-emp <= 0.5 entropy = 0.94 samples = 28 value = [18, 10] class = 0 1458->1459 1486 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1458->1486 1460 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1459->1460 1461 race_Asian <= 0.5 entropy = 0.971 samples = 25 value = [15, 10] class = 0 1459->1461 1462 age <= 33.5 entropy = 0.954 samples = 24 value = [15, 9] class = 0 1461->1462 1485 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1461->1485 1463 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1462->1463 1464 race_White <= 0.5 entropy = 0.985 samples = 21 value = [12, 9] class = 0 1462->1464 1465 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1464->1465 1466 hours-per-week <= 77.5 entropy = 0.993 samples = 20 value = [11, 9] class = 0 1464->1466 1467 education <= 10.5 entropy = 0.998 samples = 19 value = [10, 9] class = 0 1466->1467 1484 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1466->1484 1468 hours-per-week <= 73.5 entropy = 1.0 samples = 18 value = [9, 9] class = 0 1467->1468 1483 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1467->1483 1469 education <= 9.5 entropy = 0.998 samples = 17 value = [9, 8] class = 0 1468->1469 1482 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1468->1482 1470 hours-per-week <= 54.5 entropy = 0.918 samples = 9 value = [6, 3] class = 0 1469->1470 1475 hours-per-week <= 66.0 entropy = 0.954 samples = 8 value = [3, 5] class = 1 1469->1475 1471 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1470->1471 1472 age <= 34.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1470->1472 1473 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1472->1473 1474 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1472->1474 1476 age <= 34.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 1475->1476 1481 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1475->1481 1477 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1476->1477 1478 hours-per-week <= 55.0 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1476->1478 1479 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1478->1479 1480 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1478->1480 1488 hours-per-week <= 57.0 entropy = 0.998 samples = 42 value = [20, 22] class = 1 1487->1488 1515 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1487->1515 1489 hours-per-week <= 54.5 entropy = 0.971 samples = 30 value = [12, 18] class = 1 1488->1489 1508 age <= 33.5 entropy = 0.918 samples = 12 value = [8, 4] class = 0 1488->1508 1490 hours-per-week <= 52.0 entropy = 0.996 samples = 26 value = [12, 14] class = 1 1489->1490 1507 entropy = 0.0 samples = 4 value = [0, 4] class = 1 1489->1507 1491 education <= 10.5 entropy = 0.99 samples = 25 value = [11, 14] class = 1 1490->1491 1506 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1490->1506 1492 education <= 9.5 entropy = 0.971 samples = 20 value = [8, 12] class = 1 1491->1492 1503 age <= 34.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1491->1503 1493 race_Black <= 0.5 entropy = 1.0 samples = 12 value = [6, 6] class = 0 1492->1493 1500 age <= 34.5 entropy = 0.811 samples = 8 value = [2, 6] class = 1 1492->1500 1494 age <= 33.5 entropy = 0.994 samples = 11 value = [5, 6] class = 1 1493->1494 1499 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1493->1499 1495 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1494->1495 1496 age <= 34.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1494->1496 1497 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1496->1497 1498 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1496->1498 1501 entropy = 0.0 samples = 4 value = [0, 4] class = 1 1500->1501 1502 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1500->1502 1504 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1503->1504 1505 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1503->1505 1509 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1508->1509 1510 age <= 34.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 1508->1510 1511 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1510->1511 1512 education <= 9.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1510->1512 1513 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1512->1513 1514 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1512->1514 1517 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1516->1517 1518 entropy = 0.0 samples = 7 value = [7, 0] class = 0 1516->1518 1520 education <= 9.5 entropy = 0.989 samples = 1566 value = [880, 686] class = 0 1519->1520 2461 workclass_Public <= 0.5 entropy = 0.791 samples = 101 value = [77, 24] class = 0 1519->2461 1521 race_Amer-Indian <= 0.5 entropy = 0.969 samples = 924 value = [557, 367] class = 0 1520->1521 2028 hours-per-week <= 43.5 entropy = 1.0 samples = 642 value = [323, 319] class = 0 1520->2028 1522 race_Hispanic <= 0.5 entropy = 0.972 samples = 912 value = [546, 366] class = 0 1521->1522 2025 age <= 58.0 entropy = 0.414 samples = 12 value = [11, 1] class = 0 1521->2025 1523 sex_Female <= 0.5 entropy = 0.973 samples = 908 value = [542, 366] class = 0 1522->1523 2024 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1522->2024 1524 hours-per-week <= 61.5 entropy = 0.979 samples = 783 value = [458, 325] class = 0 1523->1524 1953 age <= 53.5 entropy = 0.913 samples = 125 value = [84, 41] class = 0 1523->1953 1525 hours-per-week <= 41.0 entropy = 0.983 samples = 743 value = [429, 314] class = 0 1524->1525 1928 age <= 52.5 entropy = 0.849 samples = 40 value = [29, 11] class = 0 1524->1928 1526 age <= 37.5 entropy = 0.963 samples = 462 value = [283, 179] class = 0 1525->1526 1725 hours-per-week <= 42.5 entropy = 0.999 samples = 281 value = [146, 135] class = 0 1525->1725 1527 race_White <= 0.5 entropy = 0.859 samples = 46 value = [33, 13] class = 0 1526->1527 1540 age <= 59.5 entropy = 0.97 samples = 416 value = [250, 166] class = 0 1526->1540 1528 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1527->1528 1529 hours-per-week <= 37.5 entropy = 0.893 samples = 42 value = [29, 13] class = 0 1527->1529 1530 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1529->1530 1531 workclass_Public <= 0.5 entropy = 0.872 samples = 41 value = [29, 12] class = 0 1529->1531 1532 workclass_Private <= 0.5 entropy = 0.834 samples = 34 value = [25, 9] class = 0 1531->1532 1537 age <= 36.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1531->1537 1533 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1532->1533 1534 age <= 36.5 entropy = 0.845 samples = 33 value = [24, 9] class = 0 1532->1534 1535 entropy = 0.837 samples = 15 value = [11, 4] class = 0 1534->1535 1536 entropy = 0.852 samples = 18 value = [13, 5] class = 0 1534->1536 1538 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1537->1538 1539 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1537->1539 1541 race_Asian <= 0.5 entropy = 0.976 samples = 387 value = [229, 158] class = 0 1540->1541 1712 workclass_Public <= 0.5 entropy = 0.85 samples = 29 value = [21, 8] class = 0 1540->1712 1542 age <= 52.5 entropy = 0.973 samples = 382 value = [228, 154] class = 0 1541->1542 1709 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 1541->1709 1543 age <= 38.225 entropy = 0.958 samples = 295 value = [183, 112] class = 0 1542->1543 1670 race_White <= 0.5 entropy = 0.999 samples = 87 value = [45, 42] class = 0 1542->1670 1544 workclass_Self-emp <= 0.5 entropy = 0.993 samples = 20 value = [9, 11] class = 1 1543->1544 1553 workclass_Private <= 0.5 entropy = 0.949 samples = 275 value = [174, 101] class = 0 1543->1553 1545 race_Black <= 0.5 entropy = 1.0 samples = 18 value = [9, 9] class = 0 1544->1545 1552 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1544->1552 1546 hours-per-week <= 37.5 entropy = 0.998 samples = 17 value = [9, 8] class = 0 1545->1546 1551 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1545->1551 1547 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1546->1547 1548 workclass_Private <= 0.5 entropy = 1.0 samples = 16 value = [8, 8] class = 0 1546->1548 1549 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1548->1549 1550 entropy = 1.0 samples = 14 value = [7, 7] class = 0 1548->1550 1554 age <= 46.5 entropy = 0.978 samples = 87 value = [51, 36] class = 0 1553->1554 1613 age <= 40.5 entropy = 0.93 samples = 188 value = [123, 65] class = 0 1553->1613 1555 race_Black <= 0.5 entropy = 0.907 samples = 59 value = [40, 19] class = 0 1554->1555 1594 age <= 51.5 entropy = 0.967 samples = 28 value = [11, 17] class = 1 1554->1594 1556 age <= 45.5 entropy = 0.918 samples = 57 value = [38, 19] class = 0 1555->1556 1593 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1555->1593 1557 workclass_Public <= 0.5 entropy = 0.943 samples = 50 value = [32, 18] class = 0 1556->1557 1590 workclass_Public <= 0.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 1556->1590 1558 age <= 38.725 entropy = 0.89 samples = 26 value = [18, 8] class = 0 1557->1558 1573 hours-per-week <= 39.0 entropy = 0.98 samples = 24 value = [14, 10] class = 0 1557->1573 1559 hours-per-week <= 37.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 1558->1559 1562 age <= 39.5 entropy = 0.949 samples = 19 value = [12, 7] class = 0 1558->1562 1560 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1559->1560 1561 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1559->1561 1563 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1562->1563 1564 age <= 40.5 entropy = 0.918 samples = 15 value = [10, 5] class = 0 1562->1564 1565 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1564->1565 1566 age <= 42.0 entropy = 0.961 samples = 13 value = [8, 5] class = 0 1564->1566 1567 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1566->1567 1568 age <= 43.5 entropy = 0.918 samples = 9 value = [6, 3] class = 0 1566->1568 1569 hours-per-week <= 37.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1568->1569 1572 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1568->1572 1570 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1569->1570 1571 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1569->1571 1574 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1573->1574 1575 age <= 39.5 entropy = 0.966 samples = 23 value = [14, 9] class = 0 1573->1575 1576 age <= 38.725 entropy = 0.985 samples = 7 value = [3, 4] class = 1 1575->1576 1579 age <= 40.5 entropy = 0.896 samples = 16 value = [11, 5] class = 0 1575->1579 1577 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1576->1577 1578 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1576->1578 1580 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1579->1580 1581 age <= 42.5 entropy = 0.94 samples = 14 value = [9, 5] class = 0 1579->1581 1582 age <= 41.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1581->1582 1585 age <= 44.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 1581->1585 1583 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1582->1583 1584 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1582->1584 1586 age <= 43.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 1585->1586 1589 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1585->1589 1587 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1586->1587 1588 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1586->1588 1591 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1590->1591 1592 entropy = 0.65 samples = 6 value = [5, 1] class = 0 1590->1592 1595 workclass_Public <= 0.5 entropy = 0.904 samples = 25 value = [8, 17] class = 1 1594->1595 1612 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1594->1612 1596 age <= 48.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 1595->1596 1599 age <= 47.5 entropy = 0.949 samples = 19 value = [7, 12] class = 1 1595->1599 1597 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1596->1597 1598 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1596->1598 1600 race_White <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 1599->1600 1603 race_White <= 0.5 entropy = 0.961 samples = 13 value = [5, 8] class = 1 1599->1603 1601 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1600->1601 1602 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1600->1602 1604 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1603->1604 1605 age <= 48.5 entropy = 0.881 samples = 10 value = [3, 7] class = 1 1603->1605 1606 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1605->1606 1607 age <= 50.5 entropy = 0.811 samples = 8 value = [2, 6] class = 1 1605->1607 1608 age <= 49.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 1607->1608 1611 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1607->1611 1609 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1608->1609 1610 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1608->1610 1614 race_Black <= 0.5 entropy = 0.977 samples = 39 value = [23, 16] class = 0 1613->1614 1625 age <= 49.5 entropy = 0.914 samples = 149 value = [100, 49] class = 0 1613->1625 1615 hours-per-week <= 39.0 entropy = 0.99 samples = 34 value = [19, 15] class = 0 1614->1615 1622 age <= 39.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1614->1622 1616 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1615->1616 1617 age <= 38.725 entropy = 0.994 samples = 33 value = [18, 15] class = 0 1615->1617 1618 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1617->1618 1619 age <= 39.5 entropy = 0.985 samples = 28 value = [16, 12] class = 0 1617->1619 1620 entropy = 0.991 samples = 18 value = [10, 8] class = 0 1619->1620 1621 entropy = 0.971 samples = 10 value = [6, 4] class = 0 1619->1621 1623 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1622->1623 1624 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1622->1624 1626 age <= 46.5 entropy = 0.89 samples = 114 value = [79, 35] class = 0 1625->1626 1659 age <= 50.5 entropy = 0.971 samples = 35 value = [21, 14] class = 0 1625->1659 1627 race_Black <= 0.5 entropy = 0.922 samples = 80 value = [53, 27] class = 0 1626->1627 1650 race_White <= 0.5 entropy = 0.787 samples = 34 value = [26, 8] class = 0 1626->1650 1628 age <= 42.5 entropy = 0.898 samples = 70 value = [48, 22] class = 0 1627->1628 1643 age <= 42.5 entropy = 1.0 samples = 10 value = [5, 5] class = 0 1627->1643 1629 hours-per-week <= 37.5 entropy = 0.75 samples = 28 value = [22, 6] class = 0 1628->1629 1634 hours-per-week <= 38.0 entropy = 0.959 samples = 42 value = [26, 16] class = 0 1628->1634 1630 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1629->1630 1631 age <= 41.5 entropy = 0.706 samples = 26 value = [21, 5] class = 0 1629->1631 1632 entropy = 0.787 samples = 17 value = [13, 4] class = 0 1631->1632 1633 entropy = 0.503 samples = 9 value = [8, 1] class = 0 1631->1633 1635 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1634->1635 1636 age <= 44.5 entropy = 0.971 samples = 40 value = [24, 16] class = 0 1634->1636 1637 age <= 43.5 entropy = 0.966 samples = 23 value = [14, 9] class = 0 1636->1637 1640 age <= 45.5 entropy = 0.977 samples = 17 value = [10, 7] class = 0 1636->1640 1638 entropy = 0.971 samples = 10 value = [6, 4] class = 0 1637->1638 1639 entropy = 0.961 samples = 13 value = [8, 5] class = 0 1637->1639 1641 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1640->1641 1642 entropy = 0.971 samples = 10 value = [6, 4] class = 0 1640->1642 1644 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1643->1644 1645 age <= 45.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 1643->1645 1646 age <= 43.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 1645->1646 1649 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1645->1649 1647 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1646->1647 1648 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1646->1648 1651 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1650->1651 1652 hours-per-week <= 39.0 entropy = 0.824 samples = 31 value = [23, 8] class = 0 1650->1652 1653 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1652->1653 1654 age <= 47.5 entropy = 0.85 samples = 29 value = [21, 8] class = 0 1652->1654 1655 entropy = 0.845 samples = 11 value = [8, 3] class = 0 1654->1655 1656 age <= 48.5 entropy = 0.852 samples = 18 value = [13, 5] class = 0 1654->1656 1657 entropy = 0.863 samples = 7 value = [5, 2] class = 0 1656->1657 1658 entropy = 0.845 samples = 11 value = [8, 3] class = 0 1656->1658 1660 race_Black <= 0.5 entropy = 0.971 samples = 10 value = [4, 6] class = 1 1659->1660 1665 age <= 51.5 entropy = 0.904 samples = 25 value = [17, 8] class = 0 1659->1665 1661 hours-per-week <= 39.0 entropy = 0.918 samples = 9 value = [3, 6] class = 1 1660->1661 1664 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1660->1664 1662 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1661->1662 1663 entropy = 0.954 samples = 8 value = [3, 5] class = 1 1661->1663 1666 race_White <= 0.5 entropy = 0.896 samples = 16 value = [11, 5] class = 0 1665->1666 1669 entropy = 0.918 samples = 9 value = [6, 3] class = 0 1665->1669 1667 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1666->1667 1668 entropy = 0.89 samples = 13 value = [9, 4] class = 0 1666->1668 1671 age <= 55.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 1670->1671 1674 hours-per-week <= 36.5 entropy = 1.0 samples = 79 value = [39, 40] class = 1 1670->1674 1672 entropy = 0.0 samples = 5 value = [5, 0] class = 0 1671->1672 1673 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1671->1673 1675 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1674->1675 1676 age <= 56.5 entropy = 0.999 samples = 75 value = [36, 39] class = 1 1674->1676 1677 age <= 53.5 entropy = 0.989 samples = 41 value = [18, 23] class = 1 1676->1677 1694 age <= 58.5 entropy = 0.998 samples = 34 value = [18, 16] class = 0 1676->1694 1678 workclass_Public <= 0.5 entropy = 0.997 samples = 15 value = [8, 7] class = 0 1677->1678 1685 workclass_Public <= 0.5 entropy = 0.961 samples = 26 value = [10, 16] class = 1 1677->1685 1679 hours-per-week <= 39.0 entropy = 0.996 samples = 13 value = [6, 7] class = 1 1678->1679 1684 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1678->1684 1680 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1679->1680 1681 workclass_Private <= 0.5 entropy = 0.98 samples = 12 value = [5, 7] class = 1 1679->1681 1682 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1681->1682 1683 entropy = 0.994 samples = 11 value = [5, 6] class = 1 1681->1683 1686 workclass_Self-emp <= 0.5 entropy = 0.994 samples = 22 value = [10, 12] class = 1 1685->1686 1693 entropy = 0.0 samples = 4 value = [0, 4] class = 1 1685->1693 1687 age <= 55.5 entropy = 0.985 samples = 21 value = [9, 12] class = 1 1686->1687 1692 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1686->1692 1688 age <= 54.5 entropy = 0.971 samples = 15 value = [6, 9] class = 1 1687->1688 1691 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1687->1691 1689 entropy = 0.985 samples = 7 value = [3, 4] class = 1 1688->1689 1690 entropy = 0.954 samples = 8 value = [3, 5] class = 1 1688->1690 1695 age <= 57.5 entropy = 0.959 samples = 21 value = [13, 8] class = 0 1694->1695 1704 workclass_Self-emp <= 0.5 entropy = 0.961 samples = 13 value = [5, 8] class = 1 1694->1704 1696 workclass_Private <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1695->1696 1699 workclass_Self-emp <= 0.5 entropy = 0.94 samples = 14 value = [9, 5] class = 0 1695->1699 1697 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1696->1697 1698 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1696->1698 1700 workclass_Public <= 0.5 entropy = 0.918 samples = 12 value = [8, 4] class = 0 1699->1700 1703 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1699->1703 1701 entropy = 0.918 samples = 9 value = [6, 3] class = 0 1700->1701 1702 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1700->1702 1705 workclass_Private <= 0.5 entropy = 0.994 samples = 11 value = [5, 6] class = 1 1704->1705 1708 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1704->1708 1706 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1705->1706 1707 entropy = 0.954 samples = 8 value = [3, 5] class = 1 1705->1707 1710 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1709->1710 1711 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1709->1711 1713 race_White <= 0.5 entropy = 0.877 samples = 27 value = [19, 8] class = 0 1712->1713 1724 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1712->1724 1714 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1713->1714 1715 age <= 61.5 entropy = 0.904 samples = 25 value = [17, 8] class = 0 1713->1715 1716 hours-per-week <= 37.5 entropy = 0.863 samples = 21 value = [15, 6] class = 0 1715->1716 1723 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1715->1723 1717 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1716->1717 1718 workclass_Private <= 0.5 entropy = 0.831 samples = 19 value = [14, 5] class = 0 1716->1718 1719 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1718->1719 1720 age <= 60.5 entropy = 0.874 samples = 17 value = [12, 5] class = 0 1718->1720 1721 entropy = 0.946 samples = 11 value = [7, 4] class = 0 1720->1721 1722 entropy = 0.65 samples = 6 value = [5, 1] class = 0 1720->1722 1726 entropy = 0.0 samples = 5 value = [0, 5] class = 1 1725->1726 1727 age <= 61.5 entropy = 0.998 samples = 276 value = [146, 130] class = 0 1725->1727 1728 age <= 43.5 entropy = 0.997 samples = 274 value = [146, 128] class = 0 1727->1728 1927 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1727->1927 1729 age <= 40.5 entropy = 0.979 samples = 111 value = [65, 46] class = 0 1728->1729 1804 age <= 54.5 entropy = 1.0 samples = 163 value = [81, 82] class = 1 1728->1804 1730 hours-per-week <= 54.5 entropy = 0.998 samples = 84 value = [44, 40] class = 0 1729->1730 1785 hours-per-week <= 43.5 entropy = 0.764 samples = 27 value = [21, 6] class = 0 1729->1785 1731 workclass_Public <= 0.5 entropy = 0.999 samples = 60 value = [29, 31] class = 1 1730->1731 1772 hours-per-week <= 58.0 entropy = 0.954 samples = 24 value = [15, 9] class = 0 1730->1772 1732 age <= 38.225 entropy = 1.0 samples = 58 value = [29, 29] class = 0 1731->1732 1771 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1731->1771 1733 age <= 37.5 entropy = 0.982 samples = 38 value = [22, 16] class = 0 1732->1733 1758 age <= 39.5 entropy = 0.934 samples = 20 value = [7, 13] class = 1 1732->1758 1734 workclass_Private <= 0.5 entropy = 0.995 samples = 24 value = [11, 13] class = 1 1733->1734 1749 hours-per-week <= 46.5 entropy = 0.75 samples = 14 value = [11, 3] class = 0 1733->1749 1735 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1734->1735 1736 hours-per-week <= 49.0 entropy = 0.959 samples = 21 value = [8, 13] class = 1 1734->1736 1737 hours-per-week <= 44.5 entropy = 0.996 samples = 13 value = [7, 6] class = 0 1736->1737 1746 hours-per-week <= 50.5 entropy = 0.544 samples = 8 value = [1, 7] class = 1 1736->1746 1738 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1737->1738 1739 age <= 36.5 entropy = 0.946 samples = 11 value = [7, 4] class = 0 1737->1739 1740 hours-per-week <= 46.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 1739->1740 1743 hours-per-week <= 45.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1739->1743 1741 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1740->1741 1742 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1740->1742 1744 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1743->1744 1745 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1743->1745 1747 entropy = 0.0 samples = 6 value = [0, 6] class = 1 1746->1747 1748 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1746->1748 1750 entropy = 0.0 samples = 6 value = [6, 0] class = 0 1749->1750 1751 race_White <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 1749->1751 1752 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1751->1752 1753 hours-per-week <= 49.0 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1751->1753 1754 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1753->1754 1755 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 1753->1755 1756 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1755->1756 1757 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1755->1757 1759 hours-per-week <= 49.5 entropy = 0.619 samples = 13 value = [2, 11] class = 1 1758->1759 1766 hours-per-week <= 47.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 1758->1766 1760 entropy = 0.0 samples = 6 value = [0, 6] class = 1 1759->1760 1761 age <= 38.725 entropy = 0.863 samples = 7 value = [2, 5] class = 1 1759->1761 1762 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1761->1762 1763 workclass_Self-emp <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 1761->1763 1764 entropy = 0.0 samples = 4 value = [0, 4] class = 1 1763->1764 1765 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1763->1765 1767 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1766->1767 1768 workclass_Private <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1766->1768 1769 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1768->1769 1770 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1768->1770 1773 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1772->1773 1774 age <= 37.5 entropy = 0.993 samples = 20 value = [11, 9] class = 0 1772->1774 1775 workclass_Private <= 0.5 entropy = 0.971 samples = 10 value = [4, 6] class = 1 1774->1775 1780 workclass_Private <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] class = 0 1774->1780 1776 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1775->1776 1777 age <= 36.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1775->1777 1778 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1777->1778 1779 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1777->1779 1781 entropy = 0.0 samples = 5 value = [5, 0] class = 0 1780->1781 1782 age <= 39.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1780->1782 1783 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1782->1783 1784 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1782->1784 1786 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1785->1786 1787 workclass_Self-emp <= 0.5 entropy = 0.706 samples = 26 value = [21, 5] class = 0 1785->1787 1788 hours-per-week <= 44.5 entropy = 0.831 samples = 19 value = [14, 5] class = 0 1787->1788 1803 entropy = 0.0 samples = 7 value = [7, 0] class = 0 1787->1803 1789 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1788->1789 1790 hours-per-week <= 55.0 entropy = 0.874 samples = 17 value = [12, 5] class = 0 1788->1790 1791 hours-per-week <= 48.5 entropy = 0.918 samples = 15 value = [10, 5] class = 0 1790->1791 1802 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1790->1802 1792 age <= 41.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 1791->1792 1797 hours-per-week <= 49.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1791->1797 1793 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1792->1793 1794 age <= 42.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 1792->1794 1795 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1794->1795 1796 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1794->1796 1798 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1797->1798 1799 age <= 42.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1797->1799 1800 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1799->1800 1801 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1799->1801 1805 workclass_Public <= 0.5 entropy = 0.996 samples = 110 value = [51, 59] class = 1 1804->1805 1888 workclass_Public <= 0.5 entropy = 0.987 samples = 53 value = [30, 23] class = 0 1804->1888 1806 hours-per-week <= 43.5 entropy = 0.998 samples = 105 value = [50, 55] class = 1 1805->1806 1885 age <= 51.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 1805->1885 1807 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1806->1807 1808 hours-per-week <= 44.5 entropy = 0.997 samples = 103 value = [48, 55] class = 1 1806->1808 1809 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1808->1809 1810 hours-per-week <= 45.5 entropy = 0.998 samples = 101 value = [48, 53] class = 1 1808->1810 1811 workclass_Self-emp <= 0.5 entropy = 0.934 samples = 20 value = [7, 13] class = 1 1810->1811 1830 race_Asian <= 0.5 entropy = 1.0 samples = 81 value = [41, 40] class = 0 1810->1830 1812 age <= 52.5 entropy = 0.989 samples = 16 value = [7, 9] class = 1 1811->1812 1829 entropy = 0.0 samples = 4 value = [0, 4] class = 1 1811->1829 1813 age <= 45.5 entropy = 0.971 samples = 15 value = [6, 9] class = 1 1812->1813 1828 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1812->1828 1814 age <= 44.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1813->1814 1817 age <= 46.5 entropy = 0.881 samples = 10 value = [3, 7] class = 1 1813->1817 1815 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1814->1815 1816 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1814->1816 1818 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1817->1818 1819 age <= 50.5 entropy = 0.918 samples = 9 value = [3, 6] class = 1 1817->1819 1820 age <= 49.5 entropy = 0.954 samples = 8 value = [3, 5] class = 1 1819->1820 1827 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1819->1827 1821 age <= 48.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 1820->1821 1826 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1820->1826 1822 age <= 47.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1821->1822 1825 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1821->1825 1823 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1822->1823 1824 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1822->1824 1831 age <= 51.5 entropy = 1.0 samples = 80 value = [41, 39] class = 0 1830->1831 1884 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1830->1884 1832 hours-per-week <= 46.5 entropy = 0.994 samples = 64 value = [35, 29] class = 0 1831->1832 1875 hours-per-week <= 49.0 entropy = 0.954 samples = 16 value = [6, 10] class = 1 1831->1875 1833 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1832->1833 1834 hours-per-week <= 47.5 entropy = 0.995 samples = 63 value = [34, 29] class = 0 1832->1834 1835 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1834->1835 1836 age <= 50.5 entropy = 0.993 samples = 62 value = [34, 28] class = 0 1834->1836 1837 age <= 44.5 entropy = 0.998 samples = 55 value = [29, 26] class = 0 1836->1837 1872 hours-per-week <= 54.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 1836->1872 1838 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1837->1838 1839 hours-per-week <= 51.0 entropy = 1.0 samples = 51 value = [26, 25] class = 0 1837->1839 1840 age <= 46.5 entropy = 0.983 samples = 33 value = [19, 14] class = 0 1839->1840 1861 hours-per-week <= 59.0 entropy = 0.964 samples = 18 value = [7, 11] class = 1 1839->1861 1841 age <= 45.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 1840->1841 1844 age <= 47.5 entropy = 0.999 samples = 27 value = [14, 13] class = 0 1840->1844 1842 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1841->1842 1843 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1841->1843 1845 workclass_Private <= 0.5 entropy = 0.98 samples = 12 value = [5, 7] class = 1 1844->1845 1850 age <= 48.5 entropy = 0.971 samples = 15 value = [9, 6] class = 0 1844->1850 1846 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1845->1846 1847 hours-per-week <= 49.0 entropy = 1.0 samples = 10 value = [5, 5] class = 0 1845->1847 1848 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1847->1848 1849 entropy = 0.985 samples = 7 value = [3, 4] class = 1 1847->1849 1851 hours-per-week <= 49.0 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1850->1851 1854 hours-per-week <= 49.0 entropy = 1.0 samples = 10 value = [5, 5] class = 0 1850->1854 1852 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1851->1852 1853 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1851->1853 1855 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1854->1855 1856 age <= 49.5 entropy = 0.991 samples = 9 value = [5, 4] class = 0 1854->1856 1857 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1856->1857 1858 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 1856->1858 1859 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1858->1859 1860 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1858->1860 1862 age <= 46.5 entropy = 0.592 samples = 7 value = [1, 6] class = 1 1861->1862 1865 age <= 46.5 entropy = 0.994 samples = 11 value = [6, 5] class = 0 1861->1865 1863 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1862->1863 1864 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1862->1864 1866 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1865->1866 1867 age <= 47.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 1865->1867 1868 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1867->1868 1869 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1867->1869 1870 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1869->1870 1871 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1869->1871 1873 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1872->1873 1874 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1872->1874 1876 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1875->1876 1877 hours-per-week <= 53.5 entropy = 0.918 samples = 15 value = [5, 10] class = 1 1875->1877 1878 age <= 52.5 entropy = 0.544 samples = 8 value = [1, 7] class = 1 1877->1878 1881 age <= 52.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1877->1881 1879 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1878->1879 1880 entropy = 0.0 samples = 5 value = [0, 5] class = 1 1878->1880 1882 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1881->1882 1883 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1881->1883 1886 entropy = 0.0 samples = 4 value = [0, 4] class = 1 1885->1886 1887 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1885->1887 1889 hours-per-week <= 44.5 entropy = 0.995 samples = 50 value = [27, 23] class = 0 1888->1889 1926 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1888->1926 1890 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1889->1890 1891 race_White <= 0.5 entropy = 0.989 samples = 48 value = [27, 21] class = 0 1889->1891 1892 hours-per-week <= 56.0 entropy = 0.722 samples = 5 value = [4, 1] class = 0 1891->1892 1895 hours-per-week <= 57.5 entropy = 0.996 samples = 43 value = [23, 20] class = 0 1891->1895 1893 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1892->1893 1894 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1892->1894 1896 hours-per-week <= 51.0 entropy = 0.999 samples = 31 value = [15, 16] class = 1 1895->1896 1917 age <= 59.5 entropy = 0.918 samples = 12 value = [8, 4] class = 0 1895->1917 1897 hours-per-week <= 46.5 entropy = 0.995 samples = 24 value = [13, 11] class = 0 1896->1897 1914 workclass_Private <= 0.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 1896->1914 1898 age <= 57.0 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1897->1898 1901 age <= 57.5 entropy = 0.982 samples = 19 value = [11, 8] class = 0 1897->1901 1899 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1898->1899 1900 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1898->1900 1902 hours-per-week <= 49.0 entropy = 0.991 samples = 9 value = [4, 5] class = 1 1901->1902 1909 hours-per-week <= 49.0 entropy = 0.881 samples = 10 value = [7, 3] class = 0 1901->1909 1903 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1902->1903 1904 workclass_Private <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 1902->1904 1905 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1904->1905 1906 age <= 56.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1904->1906 1907 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1906->1907 1908 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1906->1908 1910 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1909->1910 1911 workclass_Private <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 1909->1911 1912 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1911->1912 1913 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1911->1913 1915 entropy = 0.0 samples = 4 value = [0, 4] class = 1 1914->1915 1916 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1914->1916 1918 workclass_Private <= 0.5 entropy = 0.722 samples = 10 value = [8, 2] class = 0 1917->1918 1925 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1917->1925 1919 age <= 55.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 1918->1919 1924 entropy = 0.0 samples = 4 value = [4, 0] class = 0 1918->1924 1920 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1919->1920 1921 age <= 57.0 entropy = 0.971 samples = 5 value = [3, 2] class = 0 1919->1921 1922 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1921->1922 1923 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1921->1923 1929 age <= 39.5 entropy = 0.938 samples = 31 value = [20, 11] class = 0 1928->1929 1952 entropy = 0.0 samples = 9 value = [9, 0] class = 0 1928->1952 1930 hours-per-week <= 91.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 1929->1930 1933 race_Asian <= 0.5 entropy = 0.988 samples = 23 value = [13, 10] class = 0 1929->1933 1931 entropy = 0.0 samples = 6 value = [6, 0] class = 0 1930->1931 1932 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1930->1932 1934 age <= 43.5 entropy = 0.998 samples = 21 value = [11, 10] class = 0 1933->1934 1951 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1933->1951 1935 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1934->1935 1936 race_White <= 0.5 entropy = 0.982 samples = 19 value = [11, 8] class = 0 1934->1936 1937 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1936->1937 1938 workclass_Public <= 0.5 entropy = 0.964 samples = 18 value = [11, 7] class = 0 1936->1938 1939 hours-per-week <= 86.5 entropy = 0.997 samples = 15 value = [8, 7] class = 0 1938->1939 1950 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1938->1950 1940 hours-per-week <= 72.5 entropy = 0.994 samples = 11 value = [5, 6] class = 1 1939->1940 1949 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1939->1949 1941 age <= 47.0 entropy = 0.954 samples = 8 value = [5, 3] class = 0 1940->1941 1948 entropy = 0.0 samples = 3 value = [0, 3] class = 1 1940->1948 1942 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1941->1942 1947 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1941->1947 1943 age <= 45.0 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1942->1943 1946 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1942->1946 1944 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1943->1944 1945 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1943->1945 1954 workclass_Public <= 0.5 entropy = 0.979 samples = 89 value = [52, 37] class = 0 1953->1954 2013 age <= 57.5 entropy = 0.503 samples = 36 value = [32, 4] class = 0 1953->2013 1955 hours-per-week <= 41.5 entropy = 0.948 samples = 79 value = [50, 29] class = 0 1954->1955 2010 age <= 41.225 entropy = 0.722 samples = 10 value = [2, 8] class = 1 1954->2010 1956 workclass_Self-emp <= 0.5 entropy = 0.986 samples = 65 value = [37, 28] class = 0 1955->1956 2007 race_Black <= 0.5 entropy = 0.371 samples = 14 value = [13, 1] class = 0 1955->2007 1957 race_Asian <= 0.5 entropy = 0.965 samples = 59 value = [36, 23] class = 0 1956->1957 2004 age <= 42.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 1956->2004 1958 race_White <= 0.5 entropy = 0.958 samples = 58 value = [36, 22] class = 0 1957->1958 2003 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1957->2003 1959 age <= 45.5 entropy = 0.779 samples = 13 value = [10, 3] class = 0 1958->1959 1966 age <= 52.5 entropy = 0.982 samples = 45 value = [26, 19] class = 0 1958->1966 1960 entropy = 0.0 samples = 5 value = [5, 0] class = 0 1959->1960 1961 age <= 48.0 entropy = 0.954 samples = 8 value = [5, 3] class = 0 1959->1961 1962 entropy = 0.0 samples = 2 value = [0, 2] class = 1 1961->1962 1963 age <= 50.0 entropy = 0.65 samples = 6 value = [5, 1] class = 0 1961->1963 1964 entropy = 0.918 samples = 3 value = [2, 1] class = 0 1963->1964 1965 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1963->1965 1967 age <= 51.0 entropy = 0.976 samples = 44 value = [26, 18] class = 0 1966->1967 2002 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1966->2002 1968 age <= 45.5 entropy = 0.985 samples = 42 value = [24, 18] class = 0 1967->1968 2001 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1967->2001 1969 age <= 44.5 entropy = 0.951 samples = 27 value = [17, 10] class = 0 1968->1969 1990 hours-per-week <= 39.0 entropy = 0.997 samples = 15 value = [7, 8] class = 1 1968->1990 1970 age <= 38.725 entropy = 0.961 samples = 26 value = [16, 10] class = 0 1969->1970 1989 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1969->1989 1971 age <= 37.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 1970->1971 1974 age <= 39.5 entropy = 0.982 samples = 19 value = [11, 8] class = 0 1970->1974 1972 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1971->1972 1973 entropy = 0.0 samples = 3 value = [3, 0] class = 0 1971->1973 1975 hours-per-week <= 38.0 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1974->1975 1978 hours-per-week <= 37.0 entropy = 0.94 samples = 14 value = [9, 5] class = 0 1974->1978 1976 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1975->1976 1977 entropy = 0.811 samples = 4 value = [1, 3] class = 1 1975->1977 1979 entropy = 0.918 samples = 3 value = [1, 2] class = 1 1978->1979 1980 age <= 40.5 entropy = 0.845 samples = 11 value = [8, 3] class = 0 1978->1980 1981 entropy = 0.0 samples = 2 value = [2, 0] class = 0 1980->1981 1982 hours-per-week <= 39.0 entropy = 0.918 samples = 9 value = [6, 3] class = 0 1980->1982 1983 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1982->1983 1984 age <= 41.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 1982->1984 1985 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1984->1985 1986 age <= 43.0 entropy = 0.918 samples = 6 value = [4, 2] class = 0 1984->1986 1987 entropy = 0.811 samples = 4 value = [3, 1] class = 0 1986->1987 1988 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1986->1988 1991 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1990->1991 1992 age <= 48.5 entropy = 1.0 samples = 14 value = [7, 7] class = 0 1990->1992 1993 age <= 47.5 entropy = 0.991 samples = 9 value = [5, 4] class = 0 1992->1993 1998 age <= 49.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 1992->1998 1994 age <= 46.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 1993->1994 1997 entropy = 0.0 samples = 1 value = [1, 0] class = 0 1993->1997 1995 entropy = 1.0 samples = 6 value = [3, 3] class = 0 1994->1995 1996 entropy = 1.0 samples = 2 value = [1, 1] class = 0 1994->1996 1999 entropy = 0.0 samples = 1 value = [0, 1] class = 1 1998->1999 2000 entropy = 1.0 samples = 4 value = [2, 2] class = 0 1998->2000 2005 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2004->2005 2006 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2004->2006 2008 entropy = 0.0 samples = 12 value = [12, 0] class = 0 2007->2008 2009 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2007->2009 2011 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2010->2011 2012 entropy = 0.0 samples = 7 value = [0, 7] class = 1 2010->2012 2014 entropy = 0.0 samples = 12 value = [12, 0] class = 0 2013->2014 2015 hours-per-week <= 39.0 entropy = 0.65 samples = 24 value = [20, 4] class = 0 2013->2015 2016 age <= 60.0 entropy = 1.0 samples = 6 value = [3, 3] class = 0 2015->2016 2019 age <= 60.5 entropy = 0.31 samples = 18 value = [17, 1] class = 0 2015->2019 2017 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2016->2017 2018 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2016->2018 2020 age <= 59.5 entropy = 0.469 samples = 10 value = [9, 1] class = 0 2019->2020 2023 entropy = 0.0 samples = 8 value = [8, 0] class = 0 2019->2023 2021 entropy = 0.0 samples = 6 value = [6, 0] class = 0 2020->2021 2022 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2020->2022 2026 entropy = 0.0 samples = 11 value = [11, 0] class = 0 2025->2026 2027 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2025->2027 2029 age <= 47.5 entropy = 0.996 samples = 375 value = [202, 173] class = 0 2028->2029 2274 workclass_Private <= 0.5 entropy = 0.994 samples = 267 value = [121, 146] class = 1 2028->2274 2030 hours-per-week <= 39.0 entropy = 0.983 samples = 231 value = [133, 98] class = 0 2029->2030 2179 race_Asian <= 0.5 entropy = 0.999 samples = 144 value = [69, 75] class = 1 2029->2179 2031 workclass_Private <= 0.5 entropy = 0.779 samples = 13 value = [3, 10] class = 1 2030->2031 2040 education <= 10.5 entropy = 0.973 samples = 218 value = [130, 88] class = 0 2030->2040 2032 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2031->2032 2033 age <= 37.5 entropy = 0.954 samples = 8 value = [3, 5] class = 1 2031->2033 2034 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2033->2034 2035 age <= 46.0 entropy = 0.863 samples = 7 value = [2, 5] class = 1 2033->2035 2036 sex_Male <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 2035->2036 2039 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2035->2039 2037 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2036->2037 2038 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2036->2038 2041 age <= 40.5 entropy = 0.942 samples = 170 value = [109, 61] class = 0 2040->2041 2142 age <= 38.225 entropy = 0.989 samples = 48 value = [21, 27] class = 1 2040->2142 2042 age <= 38.225 entropy = 0.791 samples = 59 value = [45, 14] class = 0 2041->2042 2079 hours-per-week <= 42.5 entropy = 0.983 samples = 111 value = [64, 47] class = 0 2041->2079 2043 hours-per-week <= 41.0 entropy = 0.907 samples = 31 value = [21, 10] class = 0 2042->2043 2068 sex_Male <= 0.5 entropy = 0.592 samples = 28 value = [24, 4] class = 0 2042->2068 2044 race_Black <= 0.5 entropy = 0.863 samples = 28 value = [20, 8] class = 0 2043->2044 2067 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2043->2067 2045 age <= 37.5 entropy = 0.811 samples = 24 value = [18, 6] class = 0 2044->2045 2066 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2044->2066 2046 workclass_Public <= 0.5 entropy = 0.696 samples = 16 value = [13, 3] class = 0 2045->2046 2057 sex_Male <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2045->2057 2047 age <= 36.5 entropy = 0.503 samples = 9 value = [8, 1] class = 0 2046->2047 2052 sex_Female <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 2046->2052 2048 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2047->2048 2049 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 2047->2049 2050 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2049->2050 2051 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2049->2051 2053 age <= 36.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2052->2053 2056 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2052->2056 2054 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2053->2054 2055 entropy = 0.722 samples = 5 value = [4, 1] class = 0 2053->2055 2058 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2057->2058 2059 workclass_Self-emp <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 2057->2059 2060 workclass_Private <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 2059->2060 2065 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2059->2065 2061 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2060->2061 2062 race_White <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 2060->2062 2063 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2062->2063 2064 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2062->2064 2069 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2068->2069 2070 hours-per-week <= 41.0 entropy = 0.529 samples = 25 value = [22, 3] class = 0 2068->2070 2071 age <= 38.725 entropy = 0.439 samples = 22 value = [20, 2] class = 0 2070->2071 2078 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2070->2078 2072 workclass_Public <= 0.5 entropy = 0.722 samples = 10 value = [8, 2] class = 0 2071->2072 2077 entropy = 0.0 samples = 12 value = [12, 0] class = 0 2071->2077 2073 race_Black <= 0.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 2072->2073 2076 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2072->2076 2074 entropy = 0.863 samples = 7 value = [5, 2] class = 0 2073->2074 2075 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2073->2075 2080 workclass_Public <= 0.5 entropy = 0.981 samples = 110 value = [64, 46] class = 0 2079->2080 2141 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2079->2141 2081 age <= 44.5 entropy = 0.964 samples = 85 value = [52, 33] class = 0 2080->2081 2124 age <= 44.5 entropy = 0.999 samples = 25 value = [12, 13] class = 1 2080->2124 2082 race_Asian <= 0.5 entropy = 0.993 samples = 51 value = [28, 23] class = 0 2081->2082 2109 race_Black <= 0.5 entropy = 0.874 samples = 34 value = [24, 10] class = 0 2081->2109 2083 hours-per-week <= 41.0 entropy = 0.99 samples = 50 value = [28, 22] class = 0 2082->2083 2108 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2082->2108 2084 age <= 42.5 entropy = 0.992 samples = 49 value = [27, 22] class = 0 2083->2084 2107 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2083->2107 2085 sex_Male <= 0.5 entropy = 0.998 samples = 21 value = [10, 11] class = 1 2084->2085 2094 sex_Female <= 0.5 entropy = 0.967 samples = 28 value = [17, 11] class = 0 2084->2094 2086 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2085->2086 2087 race_Black <= 0.5 entropy = 1.0 samples = 20 value = [10, 10] class = 0 2085->2087 2088 workclass_Self-emp <= 0.5 entropy = 0.998 samples = 17 value = [9, 8] class = 0 2087->2088 2093 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2087->2093 2089 age <= 41.5 entropy = 0.985 samples = 14 value = [8, 6] class = 0 2088->2089 2092 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2088->2092 2090 entropy = 1.0 samples = 6 value = [3, 3] class = 0 2089->2090 2091 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2089->2091 2095 age <= 43.5 entropy = 0.99 samples = 25 value = [14, 11] class = 0 2094->2095 2106 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2094->2106 2096 workclass_Private <= 0.5 entropy = 1.0 samples = 14 value = [7, 7] class = 0 2095->2096 2101 workclass_Private <= 0.5 entropy = 0.946 samples = 11 value = [7, 4] class = 0 2095->2101 2097 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2096->2097 2098 race_White <= 0.5 entropy = 0.996 samples = 13 value = [6, 7] class = 1 2096->2098 2099 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2098->2099 2100 entropy = 1.0 samples = 12 value = [6, 6] class = 0 2098->2100 2102 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2101->2102 2103 race_White <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] class = 0 2101->2103 2104 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2103->2104 2105 entropy = 0.918 samples = 9 value = [6, 3] class = 0 2103->2105 2110 race_White <= 0.5 entropy = 0.784 samples = 30 value = [23, 7] class = 0 2109->2110 2123 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2109->2123 2111 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2110->2111 2112 age <= 46.5 entropy = 0.84 samples = 26 value = [19, 7] class = 0 2110->2112 2113 sex_Male <= 0.5 entropy = 0.937 samples = 17 value = [11, 6] class = 0 2112->2113 2120 workclass_Private <= 0.5 entropy = 0.503 samples = 9 value = [8, 1] class = 0 2112->2120 2114 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2113->2114 2115 age <= 45.5 entropy = 0.918 samples = 15 value = [10, 5] class = 0 2113->2115 2116 workclass_Private <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 2115->2116 2119 entropy = 0.811 samples = 8 value = [6, 2] class = 0 2115->2119 2117 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2116->2117 2118 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2116->2118 2121 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2120->2121 2122 entropy = 0.592 samples = 7 value = [6, 1] class = 0 2120->2122 2125 age <= 43.5 entropy = 0.989 samples = 16 value = [9, 7] class = 0 2124->2125 2136 age <= 46.5 entropy = 0.918 samples = 9 value = [3, 6] class = 1 2124->2136 2126 race_Black <= 0.5 entropy = 1.0 samples = 14 value = [7, 7] class = 0 2125->2126 2135 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2125->2135 2127 age <= 41.5 entropy = 0.996 samples = 13 value = [6, 7] class = 1 2126->2127 2134 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2126->2134 2128 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2127->2128 2129 sex_Male <= 0.5 entropy = 0.954 samples = 8 value = [3, 5] class = 1 2127->2129 2130 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2129->2130 2131 age <= 42.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2129->2131 2132 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2131->2132 2133 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2131->2133 2137 age <= 45.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 2136->2137 2140 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2136->2140 2138 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2137->2138 2139 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2137->2139 2143 age <= 37.5 entropy = 0.503 samples = 9 value = [1, 8] class = 1 2142->2143 2148 sex_Male <= 0.5 entropy = 1.0 samples = 39 value = [20, 19] class = 0 2142->2148 2144 age <= 36.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 2143->2144 2147 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2143->2147 2145 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2144->2145 2146 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2144->2146 2149 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2148->2149 2150 race_Asian <= 0.5 entropy = 0.991 samples = 36 value = [20, 16] class = 0 2148->2150 2151 workclass_Self-emp <= 0.5 entropy = 0.977 samples = 34 value = [20, 14] class = 0 2150->2151 2178 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2150->2178 2152 age <= 46.5 entropy = 0.967 samples = 33 value = [20, 13] class = 0 2151->2152 2177 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2151->2177 2153 age <= 45.5 entropy = 0.94 samples = 28 value = [18, 10] class = 0 2152->2153 2174 workclass_Public <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2152->2174 2154 age <= 41.5 entropy = 0.971 samples = 25 value = [15, 10] class = 0 2153->2154 2173 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2153->2173 2155 workclass_Private <= 0.5 entropy = 0.89 samples = 13 value = [9, 4] class = 0 2154->2155 2164 age <= 43.0 entropy = 1.0 samples = 12 value = [6, 6] class = 0 2154->2164 2156 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2155->2156 2157 race_Black <= 0.5 entropy = 0.946 samples = 11 value = [7, 4] class = 0 2155->2157 2158 age <= 40.5 entropy = 0.971 samples = 10 value = [6, 4] class = 0 2157->2158 2163 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2157->2163 2159 age <= 39.225 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2158->2159 2162 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2158->2162 2160 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2159->2160 2161 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2159->2161 2165 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2164->2165 2166 workclass_Private <= 0.5 entropy = 0.971 samples = 10 value = [6, 4] class = 0 2164->2166 2167 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2166->2167 2168 age <= 44.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2166->2168 2169 race_White <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2168->2169 2172 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2168->2172 2170 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2169->2170 2171 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2169->2171 2175 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2174->2175 2176 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2174->2176 2180 hours-per-week <= 37.5 entropy = 1.0 samples = 139 value = [69, 70] class = 1 2179->2180 2273 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2179->2273 2181 age <= 50.5 entropy = 0.722 samples = 10 value = [8, 2] class = 0 2180->2181 2184 age <= 59.5 entropy = 0.998 samples = 129 value = [61, 68] class = 1 2180->2184 2182 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2181->2182 2183 entropy = 0.0 samples = 6 value = [6, 0] class = 0 2181->2183 2185 sex_Male <= 0.5 entropy = 0.994 samples = 117 value = [53, 64] class = 1 2184->2185 2262 sex_Female <= 0.5 entropy = 0.918 samples = 12 value = [8, 4] class = 0 2184->2262 2186 race_White <= 0.5 entropy = 0.966 samples = 23 value = [14, 9] class = 0 2185->2186 2207 race_White <= 0.5 entropy = 0.979 samples = 94 value = [39, 55] class = 1 2185->2207 2187 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2186->2187 2188 education <= 10.5 entropy = 0.998 samples = 19 value = [10, 9] class = 0 2186->2188 2189 age <= 52.0 entropy = 0.998 samples = 17 value = [8, 9] class = 1 2188->2189 2206 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2188->2206 2190 workclass_Public <= 0.5 entropy = 0.918 samples = 9 value = [3, 6] class = 1 2189->2190 2199 workclass_Public <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2189->2199 2191 age <= 48.5 entropy = 0.954 samples = 8 value = [3, 5] class = 1 2190->2191 2198 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2190->2198 2192 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2191->2192 2193 age <= 49.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 2191->2193 2194 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2193->2194 2195 hours-per-week <= 39.0 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2193->2195 2196 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2195->2196 2197 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2195->2197 2200 workclass_Private <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 2199->2200 2205 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2199->2205 2201 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2200->2201 2202 age <= 53.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2200->2202 2203 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2202->2203 2204 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2202->2204 2208 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2207->2208 2209 age <= 54.5 entropy = 0.989 samples = 89 value = [39, 50] class = 1 2207->2209 2210 workclass_Self-emp <= 0.5 entropy = 0.999 samples = 66 value = [32, 34] class = 1 2209->2210 2245 workclass_Self-emp <= 0.5 entropy = 0.887 samples = 23 value = [7, 16] class = 1 2209->2245 2211 hours-per-week <= 41.0 entropy = 0.993 samples = 62 value = [28, 34] class = 1 2210->2211 2244 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2210->2244 2212 age <= 49.5 entropy = 0.987 samples = 60 value = [26, 34] class = 1 2211->2212 2243 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2211->2243 2213 education <= 10.5 entropy = 0.787 samples = 17 value = [4, 13] class = 1 2212->2213 2222 education <= 10.5 entropy = 1.0 samples = 43 value = [22, 21] class = 0 2212->2222 2214 age <= 48.5 entropy = 0.811 samples = 16 value = [4, 12] class = 1 2213->2214 2221 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2213->2221 2215 workclass_Private <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 2214->2215 2218 workclass_Public <= 0.5 entropy = 0.881 samples = 10 value = [3, 7] class = 1 2214->2218 2216 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2215->2216 2217 entropy = 0.722 samples = 5 value = [1, 4] class = 1 2215->2217 2219 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2218->2219 2220 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2218->2220 2223 age <= 52.5 entropy = 0.997 samples = 32 value = [15, 17] class = 1 2222->2223 2236 age <= 51.5 entropy = 0.946 samples = 11 value = [7, 4] class = 0 2222->2236 2224 age <= 50.5 entropy = 0.985 samples = 21 value = [9, 12] class = 1 2223->2224 2231 age <= 53.5 entropy = 0.994 samples = 11 value = [6, 5] class = 0 2223->2231 2225 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2224->2225 2226 workclass_Private <= 0.5 entropy = 0.991 samples = 18 value = [8, 10] class = 1 2224->2226 2227 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2226->2227 2228 age <= 51.5 entropy = 0.977 samples = 17 value = [7, 10] class = 1 2226->2228 2229 entropy = 0.971 samples = 10 value = [4, 6] class = 1 2228->2229 2230 entropy = 0.985 samples = 7 value = [3, 4] class = 1 2228->2230 2232 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2231->2232 2233 workclass_Private <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 2231->2233 2234 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2233->2234 2235 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2233->2235 2237 age <= 50.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 2236->2237 2240 age <= 52.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 2236->2240 2238 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2237->2238 2239 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2237->2239 2241 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2240->2241 2242 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2240->2242 2246 hours-per-week <= 41.5 entropy = 0.918 samples = 21 value = [7, 14] class = 1 2245->2246 2261 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2245->2261 2247 age <= 56.5 entropy = 0.934 samples = 20 value = [7, 13] class = 1 2246->2247 2260 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2246->2260 2248 age <= 55.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 2247->2248 2251 workclass_Private <= 0.5 entropy = 0.971 samples = 15 value = [6, 9] class = 1 2247->2251 2249 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2248->2249 2250 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2248->2250 2252 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2251->2252 2253 age <= 58.5 entropy = 0.961 samples = 13 value = [5, 8] class = 1 2251->2253 2254 education <= 10.5 entropy = 0.991 samples = 9 value = [4, 5] class = 1 2253->2254 2259 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2253->2259 2255 age <= 57.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2254->2255 2258 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2254->2258 2256 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2255->2256 2257 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2255->2257 2263 age <= 60.5 entropy = 0.845 samples = 11 value = [8, 3] class = 0 2262->2263 2272 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2262->2272 2264 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2263->2264 2265 education <= 10.5 entropy = 0.918 samples = 9 value = [6, 3] class = 0 2263->2265 2266 age <= 61.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 2265->2266 2271 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2265->2271 2267 workclass_Private <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2266->2267 2270 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2266->2270 2268 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2267->2268 2269 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2267->2269 2275 sex_Female <= 0.5 entropy = 0.997 samples = 111 value = [59, 52] class = 0 2274->2275 2342 age <= 60.5 entropy = 0.969 samples = 156 value = [62, 94] class = 1 2274->2342 2276 age <= 57.5 entropy = 0.999 samples = 107 value = [55, 52] class = 0 2275->2276 2341 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2275->2341 2277 hours-per-week <= 58.5 entropy = 0.995 samples = 100 value = [54, 46] class = 0 2276->2277 2338 workclass_Self-emp <= 0.5 entropy = 0.592 samples = 7 value = [1, 6] class = 1 2276->2338 2278 hours-per-week <= 53.0 entropy = 0.992 samples = 56 value = [25, 31] class = 1 2277->2278 2309 hours-per-week <= 94.5 entropy = 0.926 samples = 44 value = [29, 15] class = 0 2277->2309 2279 age <= 49.5 entropy = 0.999 samples = 46 value = [24, 22] class = 0 2278->2279 2306 age <= 55.5 entropy = 0.469 samples = 10 value = [1, 9] class = 1 2278->2306 2280 age <= 48.5 entropy = 0.981 samples = 31 value = [13, 18] class = 1 2279->2280 2297 age <= 53.5 entropy = 0.837 samples = 15 value = [11, 4] class = 0 2279->2297 2281 age <= 43.5 entropy = 0.999 samples = 27 value = [13, 14] class = 1 2280->2281 2296 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2280->2296 2282 hours-per-week <= 49.0 entropy = 0.977 samples = 17 value = [10, 7] class = 0 2281->2282 2291 hours-per-week <= 46.5 entropy = 0.881 samples = 10 value = [3, 7] class = 1 2281->2291 2283 hours-per-week <= 44.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 2282->2283 2286 age <= 37.725 entropy = 0.684 samples = 11 value = [9, 2] class = 0 2282->2286 2284 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2283->2284 2285 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2283->2285 2287 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2286->2287 2288 age <= 42.5 entropy = 0.503 samples = 9 value = [8, 1] class = 0 2286->2288 2289 entropy = 0.0 samples = 5 value = [5, 0] class = 0 2288->2289 2290 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2288->2290 2292 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2291->2292 2293 workclass_Self-emp <= 0.5 entropy = 0.544 samples = 8 value = [1, 7] class = 1 2291->2293 2294 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2293->2294 2295 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2293->2295 2298 age <= 52.5 entropy = 0.592 samples = 7 value = [6, 1] class = 0 2297->2298 2301 hours-per-week <= 49.0 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2297->2301 2299 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2298->2299 2300 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2298->2300 2302 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2301->2302 2303 age <= 55.0 entropy = 0.722 samples = 5 value = [4, 1] class = 0 2301->2303 2304 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2303->2304 2305 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2303->2305 2307 entropy = 0.0 samples = 9 value = [0, 9] class = 1 2306->2307 2308 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2306->2308 2310 age <= 43.5 entropy = 0.893 samples = 42 value = [29, 13] class = 0 2309->2310 2337 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2309->2337 2311 age <= 41.5 entropy = 0.702 samples = 21 value = [17, 4] class = 0 2310->2311 2324 age <= 44.5 entropy = 0.985 samples = 21 value = [12, 9] class = 0 2310->2324 2312 race_Asian <= 0.5 entropy = 0.811 samples = 16 value = [12, 4] class = 0 2311->2312 2323 entropy = 0.0 samples = 5 value = [5, 0] class = 0 2311->2323 2313 hours-per-week <= 71.0 entropy = 0.722 samples = 15 value = [12, 3] class = 0 2312->2313 2322 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2312->2322 2314 age <= 36.5 entropy = 0.811 samples = 12 value = [9, 3] class = 0 2313->2314 2321 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2313->2321 2315 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2314->2315 2316 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 10 value = [8, 2] class = 0 2314->2316 2317 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2316->2317 2318 hours-per-week <= 67.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 2316->2318 2319 entropy = 0.0 samples = 6 value = [6, 0] class = 0 2318->2319 2320 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2318->2320 2325 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2324->2325 2326 age <= 56.5 entropy = 0.949 samples = 19 value = [12, 7] class = 0 2324->2326 2327 age <= 50.5 entropy = 0.918 samples = 18 value = [12, 6] class = 0 2326->2327 2336 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2326->2336 2328 age <= 45.5 entropy = 0.985 samples = 14 value = [8, 6] class = 0 2327->2328 2335 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2327->2335 2329 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2328->2329 2330 education <= 10.5 entropy = 1.0 samples = 12 value = [6, 6] class = 0 2328->2330 2331 age <= 48.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2330->2331 2334 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2330->2334 2332 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2331->2332 2333 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2331->2333 2339 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2338->2339 2340 entropy = 0.0 samples = 6 value = [0, 6] class = 1 2338->2340 2343 age <= 59.5 entropy = 0.974 samples = 153 value = [62, 91] class = 1 2342->2343 2460 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2342->2460 2344 hours-per-week <= 59.0 entropy = 0.969 samples = 151 value = [60, 91] class = 1 2343->2344 2459 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2343->2459 2345 hours-per-week <= 53.0 entropy = 0.986 samples = 128 value = [55, 73] class = 1 2344->2345 2444 sex_Male <= 0.5 entropy = 0.755 samples = 23 value = [5, 18] class = 1 2344->2444 2346 hours-per-week <= 45.5 entropy = 0.953 samples = 110 value = [41, 69] class = 1 2345->2346 2433 sex_Female <= 0.5 entropy = 0.764 samples = 18 value = [14, 4] class = 0 2345->2433 2347 hours-per-week <= 44.5 entropy = 0.994 samples = 44 value = [20, 24] class = 1 2346->2347 2380 education <= 10.5 entropy = 0.902 samples = 66 value = [21, 45] class = 1 2346->2380 2348 age <= 39.725 entropy = 0.764 samples = 9 value = [2, 7] class = 1 2347->2348 2353 age <= 36.5 entropy = 0.999 samples = 35 value = [18, 17] class = 0 2347->2353 2349 age <= 36.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2348->2349 2352 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2348->2352 2350 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2349->2350 2351 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2349->2351 2354 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2353->2354 2355 sex_Male <= 0.5 entropy = 0.999 samples = 33 value = [16, 17] class = 1 2353->2355 2356 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2355->2356 2357 age <= 54.5 entropy = 0.992 samples = 29 value = [13, 16] class = 1 2355->2357 2358 age <= 50.5 entropy = 0.966 samples = 23 value = [9, 14] class = 1 2357->2358 2375 age <= 55.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2357->2375 2359 age <= 46.5 entropy = 1.0 samples = 18 value = [9, 9] class = 0 2358->2359 2374 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2358->2374 2360 age <= 45.0 entropy = 0.971 samples = 10 value = [4, 6] class = 1 2359->2360 2369 age <= 48.0 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2359->2369 2361 age <= 43.0 entropy = 1.0 samples = 8 value = [4, 4] class = 0 2360->2361 2368 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2360->2368 2362 age <= 39.5 entropy = 0.985 samples = 7 value = [3, 4] class = 1 2361->2362 2367 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2361->2367 2363 education <= 10.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2362->2363 2366 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2362->2366 2364 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2363->2364 2365 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2363->2365 2370 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2369->2370 2371 education <= 10.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2369->2371 2372 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2371->2372 2373 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2371->2373 2376 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2375->2376 2377 education <= 10.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2375->2377 2378 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2377->2378 2379 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2377->2379 2381 sex_Male <= 0.5 entropy = 0.94 samples = 56 value = [20, 36] class = 1 2380->2381 2430 age <= 52.5 entropy = 0.469 samples = 10 value = [1, 9] class = 1 2380->2430 2382 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2381->2382 2383 age <= 45.0 entropy = 0.951 samples = 54 value = [20, 34] class = 1 2381->2383 2384 age <= 43.5 entropy = 0.985 samples = 35 value = [15, 20] class = 1 2383->2384 2413 hours-per-week <= 49.0 entropy = 0.831 samples = 19 value = [5, 14] class = 1 2383->2413 2385 hours-per-week <= 47.0 entropy = 0.977 samples = 34 value = [14, 20] class = 1 2384->2385 2412 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2384->2412 2386 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2385->2386 2387 race_Amer-Indian <= 0.5 entropy = 0.983 samples = 33 value = [14, 19] class = 1 2385->2387 2388 race_Black <= 0.5 entropy = 0.989 samples = 32 value = [14, 18] class = 1 2387->2388 2411 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2387->2411 2389 age <= 41.5 entropy = 0.978 samples = 29 value = [12, 17] class = 1 2388->2389 2410 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2388->2410 2390 hours-per-week <= 51.0 entropy = 0.946 samples = 22 value = [8, 14] class = 1 2389->2390 2405 hours-per-week <= 51.0 entropy = 0.985 samples = 7 value = [4, 3] class = 0 2389->2405 2391 age <= 36.5 entropy = 0.852 samples = 18 value = [5, 13] class = 1 2390->2391 2404 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2390->2404 2392 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2391->2392 2393 age <= 38.725 entropy = 0.896 samples = 16 value = [5, 11] class = 1 2391->2393 2394 age <= 37.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 2393->2394 2397 age <= 39.5 entropy = 0.946 samples = 11 value = [4, 7] class = 1 2393->2397 2395 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2394->2395 2396 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2394->2396 2398 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2397->2398 2399 hours-per-week <= 49.0 entropy = 0.863 samples = 7 value = [2, 5] class = 1 2397->2399 2400 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2399->2400 2401 age <= 40.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2399->2401 2402 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2401->2402 2403 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2401->2403 2406 age <= 42.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2405->2406 2409 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2405->2409 2407 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2406->2407 2408 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2406->2408 2414 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2413->2414 2415 age <= 58.0 entropy = 0.852 samples = 18 value = [5, 13] class = 1 2413->2415 2416 age <= 55.0 entropy = 0.811 samples = 16 value = [4, 12] class = 1 2415->2416 2429 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2415->2429 2417 age <= 53.5 entropy = 0.863 samples = 14 value = [4, 10] class = 1 2416->2417 2428 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2416->2428 2418 age <= 49.5 entropy = 0.779 samples = 13 value = [3, 10] class = 1 2417->2418 2427 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2417->2427 2419 age <= 48.5 entropy = 0.918 samples = 9 value = [3, 6] class = 1 2418->2419 2426 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2418->2426 2420 age <= 47.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 2419->2420 2425 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2419->2425 2421 age <= 46.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2420->2421 2424 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2420->2424 2422 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2421->2422 2423 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2421->2423 2431 entropy = 0.0 samples = 9 value = [0, 9] class = 1 2430->2431 2432 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2430->2432 2434 age <= 52.5 entropy = 0.811 samples = 16 value = [12, 4] class = 0 2433->2434 2443 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2433->2443 2435 age <= 48.0 entropy = 0.863 samples = 14 value = [10, 4] class = 0 2434->2435 2442 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2434->2442 2436 age <= 38.225 entropy = 0.722 samples = 10 value = [8, 2] class = 0 2435->2436 2441 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2435->2441 2437 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2436->2437 2438 hours-per-week <= 55.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 2436->2438 2439 entropy = 0.0 samples = 6 value = [6, 0] class = 0 2438->2439 2440 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2438->2440 2445 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2444->2445 2446 hours-per-week <= 91.5 entropy = 0.684 samples = 22 value = [4, 18] class = 1 2444->2446 2447 hours-per-week <= 67.5 entropy = 0.592 samples = 21 value = [3, 18] class = 1 2446->2447 2458 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2446->2458 2448 race_White <= 0.5 entropy = 0.722 samples = 15 value = [3, 12] class = 1 2447->2448 2457 entropy = 0.0 samples = 6 value = [0, 6] class = 1 2447->2457 2449 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2448->2449 2450 age <= 51.0 entropy = 0.592 samples = 14 value = [2, 12] class = 1 2448->2450 2451 age <= 47.5 entropy = 0.684 samples = 11 value = [2, 9] class = 1 2450->2451 2456 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2450->2456 2452 hours-per-week <= 62.5 entropy = 0.503 samples = 9 value = [1, 8] class = 1 2451->2452 2455 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2451->2455 2453 entropy = 0.0 samples = 6 value = [0, 6] class = 1 2452->2453 2454 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2452->2454 2462 sex_Female <= 0.5 entropy = 0.837 samples = 90 value = [66, 24] class = 0 2461->2462 2521 entropy = 0.0 samples = 11 value = [11, 0] class = 0 2461->2521 2463 age <= 76.0 entropy = 0.903 samples = 69 value = [47, 22] class = 0 2462->2463 2514 education <= 9.5 entropy = 0.454 samples = 21 value = [19, 2] class = 0 2462->2514 2464 hours-per-week <= 36.0 entropy = 0.923 samples = 65 value = [43, 22] class = 0 2463->2464 2513 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2463->2513 2465 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2464->2465 2466 age <= 65.5 entropy = 0.895 samples = 61 value = [42, 19] class = 0 2464->2466 2467 hours-per-week <= 62.5 entropy = 0.967 samples = 33 value = [20, 13] class = 0 2466->2467 2492 hours-per-week <= 46.5 entropy = 0.75 samples = 28 value = [22, 6] class = 0 2466->2492 2468 workclass_Self-emp <= 0.5 entropy = 0.981 samples = 31 value = [18, 13] class = 0 2467->2468 2491 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2467->2491 2469 race_Black <= 0.5 entropy = 0.902 samples = 22 value = [15, 7] class = 0 2468->2469 2484 hours-per-week <= 52.0 entropy = 0.918 samples = 9 value = [3, 6] class = 1 2468->2484 2470 education <= 9.5 entropy = 0.863 samples = 21 value = [15, 6] class = 0 2469->2470 2483 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2469->2483 2471 hours-per-week <= 54.0 entropy = 0.971 samples = 15 value = [9, 6] class = 0 2470->2471 2482 entropy = 0.0 samples = 6 value = [6, 0] class = 0 2470->2482 2472 hours-per-week <= 42.5 entropy = 0.94 samples = 14 value = [9, 5] class = 0 2471->2472 2481 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2471->2481 2473 race_Asian <= 0.5 entropy = 0.994 samples = 11 value = [6, 5] class = 0 2472->2473 2480 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2472->2480 2474 age <= 64.5 entropy = 1.0 samples = 10 value = [5, 5] class = 0 2473->2474 2479 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2473->2479 2475 age <= 63.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 2474->2475 2478 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2474->2478 2476 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2475->2476 2477 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2475->2477 2485 age <= 64.0 entropy = 0.954 samples = 8 value = [3, 5] class = 1 2484->2485 2490 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2484->2490 2486 hours-per-week <= 42.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2485->2486 2489 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2485->2489 2487 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2486->2487 2488 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2486->2488 2493 race_Asian <= 0.5 entropy = 0.845 samples = 22 value = [16, 6] class = 0 2492->2493 2512 entropy = 0.0 samples = 6 value = [6, 0] class = 0 2492->2512 2494 workclass_Self-emp <= 0.5 entropy = 0.792 samples = 21 value = [16, 5] class = 0 2493->2494 2511 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2493->2511 2495 hours-per-week <= 42.5 entropy = 0.94 samples = 14 value = [9, 5] class = 0 2494->2495 2510 entropy = 0.0 samples = 7 value = [7, 0] class = 0 2494->2510 2496 age <= 73.5 entropy = 0.89 samples = 13 value = [9, 4] class = 0 2495->2496 2509 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2495->2509 2497 age <= 71.0 entropy = 0.811 samples = 12 value = [9, 3] class = 0 2496->2497 2508 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2496->2508 2498 hours-per-week <= 38.5 entropy = 0.881 samples = 10 value = [7, 3] class = 0 2497->2498 2507 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2497->2507 2499 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2498->2499 2500 age <= 69.5 entropy = 0.918 samples = 9 value = [6, 3] class = 0 2498->2500 2501 age <= 67.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 2500->2501 2506 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2500->2506 2502 education <= 9.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2501->2502 2505 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2501->2505 2503 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2502->2503 2504 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2502->2504 2515 workclass_Private <= 0.5 entropy = 0.619 samples = 13 value = [11, 2] class = 0 2514->2515 2520 entropy = 0.0 samples = 8 value = [8, 0] class = 0 2514->2520 2516 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2515->2516 2517 age <= 73.5 entropy = 0.439 samples = 11 value = [10, 1] class = 0 2515->2517 2518 entropy = 0.0 samples = 8 value = [8, 0] class = 0 2517->2518 2519 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2517->2519 2523 age <= 28.5 entropy = 0.974 samples = 794 value = [322, 472] class = 1 2522->2523 3012 age <= 33.5 entropy = 0.781 samples = 721 value = [167, 554] class = 1 2522->3012 2524 age <= 24.5 entropy = 0.784 samples = 60 value = [46, 14] class = 0 2523->2524 2557 hours-per-week <= 31.0 entropy = 0.955 samples = 734 value = [276, 458] class = 1 2523->2557 2525 entropy = 0.0 samples = 11 value = [11, 0] class = 0 2524->2525 2526 sex_Male <= 0.5 entropy = 0.863 samples = 49 value = [35, 14] class = 0 2524->2526 2527 hours-per-week <= 22.5 entropy = 1.0 samples = 14 value = [7, 7] class = 0 2526->2527 2538 education <= 12.5 entropy = 0.722 samples = 35 value = [28, 7] class = 0 2526->2538 2528 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2527->2528 2529 education <= 12.5 entropy = 0.98 samples = 12 value = [7, 5] class = 0 2527->2529 2530 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2529->2530 2531 hours-per-week <= 35.0 entropy = 1.0 samples = 10 value = [5, 5] class = 0 2529->2531 2532 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2531->2532 2533 education <= 13.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2531->2533 2534 age <= 25.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 2533->2534 2537 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2533->2537 2535 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2534->2535 2536 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2534->2536 2539 entropy = 0.0 samples = 7 value = [7, 0] class = 0 2538->2539 2540 education <= 13.5 entropy = 0.811 samples = 28 value = [21, 7] class = 0 2538->2540 2541 hours-per-week <= 34.0 entropy = 0.855 samples = 25 value = [18, 7] class = 0 2540->2541 2556 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2540->2556 2542 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2541->2542 2543 race_White <= 0.5 entropy = 0.902 samples = 22 value = [15, 7] class = 0 2541->2543 2544 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2543->2544 2545 hours-per-week <= 40.5 entropy = 0.918 samples = 21 value = [14, 7] class = 0 2543->2545 2546 workclass_Public <= 0.5 entropy = 0.934 samples = 20 value = [13, 7] class = 0 2545->2546 2555 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2545->2555 2547 age <= 25.5 entropy = 0.874 samples = 17 value = [12, 5] class = 0 2546->2547 2554 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2546->2554 2548 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2547->2548 2549 age <= 26.5 entropy = 0.918 samples = 15 value = [10, 5] class = 0 2547->2549 2550 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2549->2550 2551 age <= 27.5 entropy = 0.811 samples = 12 value = [9, 3] class = 0 2549->2551 2552 entropy = 0.722 samples = 5 value = [4, 1] class = 0 2551->2552 2553 entropy = 0.863 samples = 7 value = [5, 2] class = 0 2551->2553 2558 sex_Male <= 0.5 entropy = 0.97 samples = 108 value = [65, 43] class = 0 2557->2558 2629 age <= 36.5 entropy = 0.922 samples = 626 value = [211, 415] class = 1 2557->2629 2559 age <= 67.5 entropy = 0.998 samples = 38 value = [18, 20] class = 1 2558->2559 2588 education <= 14.5 entropy = 0.913 samples = 70 value = [47, 23] class = 0 2558->2588 2560 education <= 14.5 entropy = 0.977 samples = 34 value = [14, 20] class = 1 2559->2560 2587 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2559->2587 2561 education <= 13.5 entropy = 0.954 samples = 32 value = [12, 20] class = 1 2560->2561 2586 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2560->2586 2562 hours-per-week <= 11.5 entropy = 0.985 samples = 28 value = [12, 16] class = 1 2561->2562 2585 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2561->2585 2563 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2562->2563 2564 age <= 51.5 entropy = 0.996 samples = 26 value = [12, 14] class = 1 2562->2564 2565 age <= 45.5 entropy = 0.976 samples = 22 value = [9, 13] class = 1 2564->2565 2584 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2564->2584 2566 hours-per-week <= 24.5 entropy = 0.993 samples = 20 value = [9, 11] class = 1 2565->2566 2583 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2565->2583 2567 hours-per-week <= 22.0 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2566->2567 2572 age <= 42.0 entropy = 0.918 samples = 12 value = [4, 8] class = 1 2566->2572 2568 workclass_Private <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 2567->2568 2571 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2567->2571 2569 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2568->2569 2570 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2568->2570 2573 race_Black <= 0.5 entropy = 0.845 samples = 11 value = [3, 8] class = 1 2572->2573 2582 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2572->2582 2574 age <= 36.5 entropy = 0.722 samples = 10 value = [2, 8] class = 1 2573->2574 2581 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2573->2581 2575 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2574->2575 2576 education <= 12.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2574->2576 2577 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2576->2577 2578 hours-per-week <= 27.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 2576->2578 2579 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2578->2579 2580 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2578->2580 2589 age <= 42.5 entropy = 0.811 samples = 56 value = [42, 14] class = 0 2588->2589 2620 age <= 55.5 entropy = 0.94 samples = 14 value = [5, 9] class = 1 2588->2620 2590 hours-per-week <= 24.5 entropy = 0.323 samples = 17 value = [16, 1] class = 0 2589->2590 2595 education <= 12.5 entropy = 0.918 samples = 39 value = [26, 13] class = 0 2589->2595 2591 entropy = 0.0 samples = 9 value = [9, 0] class = 0 2590->2591 2592 hours-per-week <= 27.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 2590->2592 2593 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2592->2593 2594 entropy = 0.0 samples = 5 value = [5, 0] class = 0 2592->2594 2596 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2595->2596 2597 workclass_Self-emp <= 0.5 entropy = 0.952 samples = 35 value = [22, 13] class = 0 2595->2597 2598 hours-per-week <= 28.0 entropy = 0.998 samples = 17 value = [8, 9] class = 1 2597->2598 2609 age <= 62.0 entropy = 0.764 samples = 18 value = [14, 4] class = 0 2597->2609 2599 hours-per-week <= 10.5 entropy = 0.94 samples = 14 value = [5, 9] class = 1 2598->2599 2608 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2598->2608 2600 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2599->2600 2601 workclass_Public <= 0.5 entropy = 0.994 samples = 11 value = [5, 6] class = 1 2599->2601 2602 hours-per-week <= 13.5 entropy = 0.918 samples = 9 value = [3, 6] class = 1 2601->2602 2607 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2601->2607 2603 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2602->2603 2604 hours-per-week <= 22.0 entropy = 0.811 samples = 8 value = [2, 6] class = 1 2602->2604 2605 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2604->2605 2606 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2604->2606 2610 entropy = 0.0 samples = 8 value = [8, 0] class = 0 2609->2610 2611 age <= 63.5 entropy = 0.971 samples = 10 value = [6, 4] class = 0 2609->2611 2612 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2611->2612 2613 age <= 66.0 entropy = 0.918 samples = 9 value = [6, 3] class = 0 2611->2613 2614 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2613->2614 2615 hours-per-week <= 9.0 entropy = 0.985 samples = 7 value = [4, 3] class = 0 2613->2615 2616 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2615->2616 2617 hours-per-week <= 11.0 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2615->2617 2618 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2617->2618 2619 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2617->2619 2621 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2620->2621 2622 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 10 value = [5, 5] class = 0 2620->2622 2623 age <= 57.0 entropy = 0.722 samples = 5 value = [1, 4] class = 1 2622->2623 2626 age <= 67.0 entropy = 0.722 samples = 5 value = [4, 1] class = 0 2622->2626 2624 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2623->2624 2625 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2623->2625 2627 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2626->2627 2628 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2626->2628 2630 race_Hispanic <= 0.5 entropy = 0.988 samples = 163 value = [71, 92] class = 1 2629->2630 2731 race_Asian <= 0.5 entropy = 0.884 samples = 463 value = [140, 323] class = 1 2629->2731 2631 hours-per-week <= 34.0 entropy = 0.985 samples = 161 value = [69, 92] class = 1 2630->2631 2730 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2630->2730 2632 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2631->2632 2633 sex_Female <= 0.5 entropy = 0.987 samples = 159 value = [69, 90] class = 1 2631->2633 2634 hours-per-week <= 35.5 entropy = 0.994 samples = 132 value = [60, 72] class = 1 2633->2634 2709 age <= 30.5 entropy = 0.918 samples = 27 value = [9, 18] class = 1 2633->2709 2635 race_Asian <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 2634->2635 2638 workclass_Public <= 0.5 entropy = 0.99 samples = 127 value = [56, 71] class = 1 2634->2638 2636 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2635->2636 2637 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2635->2637 2639 hours-per-week <= 37.5 entropy = 0.981 samples = 105 value = [44, 61] class = 1 2638->2639 2696 age <= 33.5 entropy = 0.994 samples = 22 value = [12, 10] class = 0 2638->2696 2640 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2639->2640 2641 age <= 34.5 entropy = 0.985 samples = 103 value = [44, 59] class = 1 2639->2641 2642 age <= 33.5 entropy = 0.969 samples = 78 value = [31, 47] class = 1 2641->2642 2681 education <= 15.0 entropy = 0.999 samples = 25 value = [13, 12] class = 0 2641->2681 2643 education <= 14.5 entropy = 0.987 samples = 67 value = [29, 38] class = 1 2642->2643 2678 education <= 13.5 entropy = 0.684 samples = 11 value = [2, 9] class = 1 2642->2678 2644 education <= 13.5 entropy = 0.976 samples = 61 value = [25, 36] class = 1 2643->2644 2673 race_Asian <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2643->2673 2645 hours-per-week <= 39.0 entropy = 0.987 samples = 53 value = [23, 30] class = 1 2644->2645 2668 workclass_Private <= 0.5 entropy = 0.811 samples = 8 value = [2, 6] class = 1 2644->2668 2646 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2645->2646 2647 race_Asian <= 0.5 entropy = 0.99 samples = 52 value = [23, 29] class = 1 2645->2647 2648 age <= 30.5 entropy = 0.981 samples = 50 value = [21, 29] class = 1 2647->2648 2667 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2647->2667 2649 age <= 29.5 entropy = 0.997 samples = 15 value = [7, 8] class = 1 2648->2649 2652 age <= 31.5 entropy = 0.971 samples = 35 value = [14, 21] class = 1 2648->2652 2650 entropy = 0.954 samples = 8 value = [3, 5] class = 1 2649->2650 2651 entropy = 0.985 samples = 7 value = [4, 3] class = 0 2649->2651 2653 education <= 12.5 entropy = 0.881 samples = 10 value = [3, 7] class = 1 2652->2653 2656 education <= 12.5 entropy = 0.99 samples = 25 value = [11, 14] class = 1 2652->2656 2654 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2653->2654 2655 entropy = 0.954 samples = 8 value = [3, 5] class = 1 2653->2655 2657 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2656->2657 2658 race_White <= 0.5 entropy = 0.966 samples = 23 value = [9, 14] class = 1 2656->2658 2659 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2658->2659 2660 age <= 32.5 entropy = 0.959 samples = 21 value = [8, 13] class = 1 2658->2660 2661 workclass_Self-emp <= 0.5 entropy = 0.994 samples = 11 value = [5, 6] class = 1 2660->2661 2664 workclass_Self-emp <= 0.5 entropy = 0.881 samples = 10 value = [3, 7] class = 1 2660->2664 2662 entropy = 0.971 samples = 10 value = [4, 6] class = 1 2661->2662 2663 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2661->2663 2665 entropy = 0.954 samples = 8 value = [3, 5] class = 1 2664->2665 2666 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2664->2666 2669 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2668->2669 2670 hours-per-week <= 39.0 entropy = 0.592 samples = 7 value = [1, 6] class = 1 2668->2670 2671 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2670->2671 2672 entropy = 0.0 samples = 6 value = [0, 6] class = 1 2670->2672 2674 workclass_Private <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 2673->2674 2677 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2673->2677 2675 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2674->2675 2676 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2674->2676 2679 entropy = 0.503 samples = 9 value = [1, 8] class = 1 2678->2679 2680 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2678->2680 2682 hours-per-week <= 39.0 entropy = 0.988 samples = 23 value = [13, 10] class = 0 2681->2682 2695 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2681->2695 2683 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2682->2683 2684 workclass_Self-emp <= 0.5 entropy = 0.994 samples = 22 value = [12, 10] class = 0 2682->2684 2685 education <= 12.5 entropy = 0.982 samples = 19 value = [11, 8] class = 0 2684->2685 2694 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2684->2694 2686 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2685->2686 2687 age <= 35.5 entropy = 0.954 samples = 16 value = [10, 6] class = 0 2685->2687 2688 education <= 13.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 2687->2688 2691 race_White <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 2687->2691 2689 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2688->2689 2690 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2688->2690 2692 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2691->2692 2693 entropy = 1.0 samples = 6 value = [3, 3] class = 0 2691->2693 2697 education <= 13.5 entropy = 0.918 samples = 15 value = [10, 5] class = 0 2696->2697 2706 age <= 34.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 2696->2706 2698 age <= 30.5 entropy = 0.98 samples = 12 value = [7, 5] class = 0 2697->2698 2705 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2697->2705 2699 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2698->2699 2700 race_White <= 0.5 entropy = 0.811 samples = 8 value = [6, 2] class = 0 2698->2700 2701 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2700->2701 2702 education <= 12.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2700->2702 2703 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2702->2703 2704 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2702->2704 2707 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2706->2707 2708 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2706->2708 2710 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2709->2710 2711 age <= 35.5 entropy = 0.976 samples = 22 value = [9, 13] class = 1 2709->2711 2712 age <= 32.5 entropy = 0.998 samples = 19 value = [9, 10] class = 1 2711->2712 2729 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2711->2729 2713 age <= 31.5 entropy = 0.811 samples = 8 value = [2, 6] class = 1 2712->2713 2718 hours-per-week <= 37.5 entropy = 0.946 samples = 11 value = [7, 4] class = 0 2712->2718 2714 education <= 12.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2713->2714 2717 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2713->2717 2715 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2714->2715 2716 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2714->2716 2719 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2718->2719 2720 age <= 34.5 entropy = 0.881 samples = 10 value = [7, 3] class = 0 2718->2720 2721 race_Black <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 2720->2721 2728 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2720->2728 2722 workclass_Private <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 2721->2722 2727 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2721->2727 2723 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2722->2723 2724 education <= 12.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2722->2724 2725 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2724->2725 2726 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2724->2726 2732 sex_Male <= 0.5 entropy = 0.86 samples = 431 value = [122, 309] class = 1 2731->2732 2987 sex_Female <= 0.5 entropy = 0.989 samples = 32 value = [18, 14] class = 0 2731->2987 2733 age <= 53.0 entropy = 0.976 samples = 61 value = [25, 36] class = 1 2732->2733 2776 education <= 15.5 entropy = 0.83 samples = 370 value = [97, 273] class = 1 2732->2776 2734 education <= 12.5 entropy = 0.902 samples = 44 value = [14, 30] class = 1 2733->2734 2763 age <= 56.5 entropy = 0.937 samples = 17 value = [11, 6] class = 0 2733->2763 2735 hours-per-week <= 37.5 entropy = 0.439 samples = 11 value = [1, 10] class = 1 2734->2735 2738 workclass_Self-emp <= 0.5 entropy = 0.967 samples = 33 value = [13, 20] class = 1 2734->2738 2736 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2735->2736 2737 entropy = 0.0 samples = 8 value = [0, 8] class = 1 2735->2737 2739 age <= 43.5 entropy = 0.938 samples = 31 value = [11, 20] class = 1 2738->2739 2762 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2738->2762 2740 hours-per-week <= 38.5 entropy = 0.993 samples = 20 value = [9, 11] class = 1 2739->2740 2757 education <= 13.5 entropy = 0.684 samples = 11 value = [2, 9] class = 1 2739->2757 2741 age <= 37.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 2740->2741 2744 age <= 39.5 entropy = 0.997 samples = 15 value = [8, 7] class = 0 2740->2744 2742 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2741->2742 2743 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2741->2743 2745 education <= 13.5 entropy = 0.954 samples = 8 value = [3, 5] class = 1 2744->2745 2752 age <= 40.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 2744->2752 2746 age <= 37.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 2745->2746 2751 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2745->2751 2747 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2746->2747 2748 workclass_Private <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2746->2748 2749 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2748->2749 2750 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2748->2750 2753 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2752->2753 2754 race_White <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2752->2754 2755 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2754->2755 2756 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2754->2756 2758 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2757->2758 2759 age <= 50.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2757->2759 2760 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2759->2760 2761 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2759->2761 2764 entropy = 0.0 samples = 5 value = [5, 0] class = 0 2763->2764 2765 education <= 12.5 entropy = 1.0 samples = 12 value = [6, 6] class = 0 2763->2765 2766 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2765->2766 2767 workclass_Self-emp <= 0.5 entropy = 0.971 samples = 10 value = [4, 6] class = 1 2765->2767 2768 race_White <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 2767->2768 2775 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2767->2775 2769 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2768->2769 2770 hours-per-week <= 37.0 entropy = 0.985 samples = 7 value = [4, 3] class = 0 2768->2770 2771 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2770->2771 2772 age <= 61.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2770->2772 2773 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2772->2773 2774 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2772->2774 2777 race_Amer-Indian <= 0.5 entropy = 0.846 samples = 355 value = [97, 258] class = 1 2776->2777 2986 entropy = 0.0 samples = 15 value = [0, 15] class = 1 2776->2986 2778 education <= 13.5 entropy = 0.84 samples = 353 value = [95, 258] class = 1 2777->2778 2985 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2777->2985 2779 race_Hispanic <= 0.5 entropy = 0.886 samples = 237 value = [72, 165] class = 1 2778->2779 2922 age <= 82.0 entropy = 0.718 samples = 116 value = [23, 93] class = 1 2778->2922 2780 age <= 41.5 entropy = 0.882 samples = 236 value = [71, 165] class = 1 2779->2780 2921 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2779->2921 2781 age <= 37.5 entropy = 0.96 samples = 60 value = [23, 37] class = 1 2780->2781 2812 workclass_Public <= 0.5 entropy = 0.845 samples = 176 value = [48, 128] class = 1 2780->2812 2782 education <= 12.5 entropy = 0.544 samples = 8 value = [1, 7] class = 1 2781->2782 2785 race_White <= 0.5 entropy = 0.983 samples = 52 value = [22, 30] class = 1 2781->2785 2783 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2782->2783 2784 entropy = 0.0 samples = 6 value = [0, 6] class = 1 2782->2784 2786 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2785->2786 2787 age <= 38.725 entropy = 0.977 samples = 51 value = [21, 30] class = 1 2785->2787 2788 workclass_Public <= 0.5 entropy = 0.9 samples = 19 value = [6, 13] class = 1 2787->2788 2797 hours-per-week <= 38.0 entropy = 0.997 samples = 32 value = [15, 17] class = 1 2787->2797 2789 hours-per-week <= 37.5 entropy = 0.852 samples = 18 value = [5, 13] class = 1 2788->2789 2796 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2788->2796 2790 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2789->2790 2791 workclass_Self-emp <= 0.5 entropy = 0.787 samples = 17 value = [4, 13] class = 1 2789->2791 2792 age <= 38.225 entropy = 0.837 samples = 15 value = [4, 11] class = 1 2791->2792 2795 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2791->2795 2793 entropy = 0.811 samples = 8 value = [2, 6] class = 1 2792->2793 2794 entropy = 0.863 samples = 7 value = [2, 5] class = 1 2792->2794 2798 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2797->2798 2799 workclass_Public <= 0.5 entropy = 1.0 samples = 30 value = [15, 15] class = 0 2797->2799 2800 education <= 12.5 entropy = 0.996 samples = 26 value = [14, 12] class = 0 2799->2800 2811 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2799->2811 2801 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2800->2801 2802 workclass_Self-emp <= 0.5 entropy = 0.994 samples = 22 value = [10, 12] class = 1 2800->2802 2803 age <= 40.5 entropy = 0.977 samples = 17 value = [7, 10] class = 1 2802->2803 2808 age <= 40.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2802->2808 2804 age <= 39.5 entropy = 0.994 samples = 11 value = [5, 6] class = 1 2803->2804 2807 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2803->2807 2805 entropy = 0.985 samples = 7 value = [3, 4] class = 1 2804->2805 2806 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2804->2806 2809 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2808->2809 2810 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2808->2810 2813 age <= 51.5 entropy = 0.778 samples = 126 value = [29, 97] class = 1 2812->2813 2886 age <= 60.5 entropy = 0.958 samples = 50 value = [19, 31] class = 1 2812->2886 2814 age <= 49.5 entropy = 0.7 samples = 74 value = [14, 60] class = 1 2813->2814 2853 education <= 12.5 entropy = 0.867 samples = 52 value = [15, 37] class = 1 2813->2853 2815 race_Black <= 0.5 entropy = 0.752 samples = 65 value = [14, 51] class = 1 2814->2815 2852 entropy = 0.0 samples = 9 value = [0, 9] class = 1 2814->2852 2816 workclass_Self-emp <= 0.5 entropy = 0.777 samples = 61 value = [14, 47] class = 1 2815->2816 2851 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2815->2851 2817 age <= 44.5 entropy = 0.696 samples = 48 value = [9, 39] class = 1 2816->2817 2842 age <= 44.5 entropy = 0.961 samples = 13 value = [5, 8] class = 1 2816->2842 2818 age <= 43.5 entropy = 0.426 samples = 23 value = [2, 21] class = 1 2817->2818 2825 hours-per-week <= 36.0 entropy = 0.855 samples = 25 value = [7, 18] class = 1 2817->2825 2819 education <= 12.5 entropy = 0.567 samples = 15 value = [2, 13] class = 1 2818->2819 2824 entropy = 0.0 samples = 8 value = [0, 8] class = 1 2818->2824 2820 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2819->2820 2821 age <= 42.5 entropy = 0.592 samples = 14 value = [2, 12] class = 1 2819->2821 2822 entropy = 0.65 samples = 6 value = [1, 5] class = 1 2821->2822 2823 entropy = 0.544 samples = 8 value = [1, 7] class = 1 2821->2823 2826 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2825->2826 2827 hours-per-week <= 38.5 entropy = 0.871 samples = 24 value = [7, 17] class = 1 2825->2827 2828 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2827->2828 2829 age <= 46.5 entropy = 0.828 samples = 23 value = [6, 17] class = 1 2827->2829 2830 education <= 12.5 entropy = 0.75 samples = 14 value = [3, 11] class = 1 2829->2830 2837 education <= 12.5 entropy = 0.918 samples = 9 value = [3, 6] class = 1 2829->2837 2831 age <= 45.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2830->2831 2834 age <= 45.5 entropy = 0.503 samples = 9 value = [1, 8] class = 1 2830->2834 2832 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2831->2832 2833 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2831->2833 2835 entropy = 0.0 samples = 4 value = [0, 4] class = 1 2834->2835 2836 entropy = 0.722 samples = 5 value = [1, 4] class = 1 2834->2836 2838 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2837->2838 2839 age <= 48.5 entropy = 0.954 samples = 8 value = [3, 5] class = 1 2837->2839 2840 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2839->2840 2841 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2839->2841 2843 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2842->2843 2844 education <= 12.5 entropy = 0.845 samples = 11 value = [3, 8] class = 1 2842->2844 2845 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2844->2845 2846 age <= 45.5 entropy = 0.722 samples = 10 value = [2, 8] class = 1 2844->2846 2847 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2846->2847 2848 age <= 46.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 2846->2848 2849 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2848->2849 2850 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2848->2850 2854 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2853->2854 2855 age <= 65.0 entropy = 0.903 samples = 47 value = [15, 32] class = 1 2853->2855 2856 workclass_Private <= 0.5 entropy = 0.926 samples = 44 value = [15, 29] class = 1 2855->2856 2885 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2855->2885 2857 age <= 53.5 entropy = 0.65 samples = 12 value = [2, 10] class = 1 2856->2857 2862 age <= 63.5 entropy = 0.974 samples = 32 value = [13, 19] class = 1 2856->2862 2858 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2857->2858 2859 age <= 63.5 entropy = 0.469 samples = 10 value = [1, 9] class = 1 2857->2859 2860 entropy = 0.0 samples = 8 value = [0, 8] class = 1 2859->2860 2861 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2859->2861 2863 age <= 61.0 entropy = 0.987 samples = 30 value = [13, 17] class = 1 2862->2863 2884 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2862->2884 2864 age <= 56.0 entropy = 0.954 samples = 24 value = [9, 15] class = 1 2863->2864 2879 hours-per-week <= 37.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2863->2879 2865 hours-per-week <= 37.5 entropy = 1.0 samples = 12 value = [6, 6] class = 0 2864->2865 2874 age <= 57.5 entropy = 0.811 samples = 12 value = [3, 9] class = 1 2864->2874 2866 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2865->2866 2867 age <= 53.5 entropy = 0.994 samples = 11 value = [6, 5] class = 0 2865->2867 2868 race_White <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2867->2868 2871 age <= 54.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 2867->2871 2869 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2868->2869 2870 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2868->2870 2872 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2871->2872 2873 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2871->2873 2875 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2874->2875 2876 age <= 59.0 entropy = 0.985 samples = 7 value = [3, 4] class = 1 2874->2876 2877 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2876->2877 2878 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2876->2878 2880 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2879->2880 2881 age <= 62.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2879->2881 2882 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2881->2882 2883 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2881->2883 2887 age <= 56.5 entropy = 0.925 samples = 47 value = [16, 31] class = 1 2886->2887 2920 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2886->2920 2888 education <= 12.5 entropy = 0.959 samples = 42 value = [16, 26] class = 1 2887->2888 2919 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2887->2919 2889 age <= 49.0 entropy = 0.985 samples = 7 value = [4, 3] class = 0 2888->2889 2894 age <= 48.0 entropy = 0.928 samples = 35 value = [12, 23] class = 1 2888->2894 2890 race_White <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2889->2890 2893 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2889->2893 2891 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2890->2891 2892 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2890->2892 2895 age <= 46.5 entropy = 0.998 samples = 17 value = [8, 9] class = 1 2894->2895 2910 age <= 51.5 entropy = 0.764 samples = 18 value = [4, 14] class = 1 2894->2910 2896 hours-per-week <= 36.0 entropy = 0.94 samples = 14 value = [5, 9] class = 1 2895->2896 2909 entropy = 0.0 samples = 3 value = [3, 0] class = 0 2895->2909 2897 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2896->2897 2898 hours-per-week <= 38.5 entropy = 0.961 samples = 13 value = [5, 8] class = 1 2896->2898 2899 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2898->2899 2900 age <= 45.5 entropy = 0.918 samples = 12 value = [4, 8] class = 1 2898->2900 2901 age <= 44.0 entropy = 0.991 samples = 9 value = [4, 5] class = 1 2900->2901 2908 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2900->2908 2902 race_Black <= 0.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 2901->2902 2907 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2901->2907 2903 age <= 42.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2902->2903 2906 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2902->2906 2904 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2903->2904 2905 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2903->2905 2911 entropy = 0.0 samples = 8 value = [0, 8] class = 1 2910->2911 2912 hours-per-week <= 36.5 entropy = 0.971 samples = 10 value = [4, 6] class = 1 2910->2912 2913 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2912->2913 2914 age <= 54.5 entropy = 0.918 samples = 9 value = [3, 6] class = 1 2912->2914 2915 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2914->2915 2916 age <= 55.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 2914->2916 2917 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2916->2917 2918 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2916->2918 2923 age <= 37.5 entropy = 0.704 samples = 115 value = [22, 93] class = 1 2922->2923 2984 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2922->2984 2924 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2923->2924 2925 age <= 38.725 entropy = 0.677 samples = 112 value = [20, 92] class = 1 2923->2925 2926 entropy = 0.0 samples = 9 value = [0, 9] class = 1 2925->2926 2927 age <= 41.5 entropy = 0.71 samples = 103 value = [20, 83] class = 1 2925->2927 2928 workclass_Self-emp <= 0.5 entropy = 0.874 samples = 17 value = [5, 12] class = 1 2927->2928 2939 age <= 43.5 entropy = 0.668 samples = 86 value = [15, 71] class = 1 2927->2939 2929 education <= 14.5 entropy = 0.811 samples = 16 value = [4, 12] class = 1 2928->2929 2938 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2928->2938 2930 age <= 40.5 entropy = 0.89 samples = 13 value = [4, 9] class = 1 2929->2930 2937 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2929->2937 2931 workclass_Public <= 0.5 entropy = 0.764 samples = 9 value = [2, 7] class = 1 2930->2931 2936 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2930->2936 2932 age <= 39.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 2931->2932 2935 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2931->2935 2933 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2932->2933 2934 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2932->2934 2940 entropy = 0.0 samples = 12 value = [0, 12] class = 1 2939->2940 2941 age <= 51.5 entropy = 0.727 samples = 74 value = [15, 59] class = 1 2939->2941 2942 age <= 49.5 entropy = 0.65 samples = 48 value = [8, 40] class = 1 2941->2942 2969 age <= 52.5 entropy = 0.84 samples = 26 value = [7, 19] class = 1 2941->2969 2943 education <= 14.5 entropy = 0.712 samples = 41 value = [8, 33] class = 1 2942->2943 2968 entropy = 0.0 samples = 7 value = [0, 7] class = 1 2942->2968 2944 race_Black <= 0.5 entropy = 0.764 samples = 36 value = [8, 28] class = 1 2943->2944 2967 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2943->2967 2945 workclass_Private <= 0.5 entropy = 0.734 samples = 34 value = [7, 27] class = 1 2944->2945 2966 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2944->2966 2946 age <= 47.5 entropy = 0.831 samples = 19 value = [5, 14] class = 1 2945->2946 2961 age <= 47.5 entropy = 0.567 samples = 15 value = [2, 13] class = 1 2945->2961 2947 hours-per-week <= 40.5 entropy = 0.94 samples = 14 value = [5, 9] class = 1 2946->2947 2960 entropy = 0.0 samples = 5 value = [0, 5] class = 1 2946->2960 2948 hours-per-week <= 36.5 entropy = 0.961 samples = 13 value = [5, 8] class = 1 2947->2948 2959 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2947->2959 2949 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2948->2949 2950 age <= 44.5 entropy = 0.946 samples = 11 value = [4, 7] class = 1 2948->2950 2951 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 2950->2951 2954 workclass_Public <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 2950->2954 2952 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2951->2952 2953 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2951->2953 2955 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2954->2955 2956 hours-per-week <= 39.0 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2954->2956 2957 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2956->2957 2958 entropy = 0.811 samples = 4 value = [3, 1] class = 0 2956->2958 2962 entropy = 0.0 samples = 10 value = [0, 10] class = 1 2961->2962 2963 age <= 48.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2961->2963 2964 entropy = 0.918 samples = 3 value = [1, 2] class = 1 2963->2964 2965 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2963->2965 2970 entropy = 0.918 samples = 3 value = [2, 1] class = 0 2969->2970 2971 workclass_Public <= 0.5 entropy = 0.755 samples = 23 value = [5, 18] class = 1 2969->2971 2972 age <= 54.5 entropy = 0.874 samples = 17 value = [5, 12] class = 1 2971->2972 2983 entropy = 0.0 samples = 6 value = [0, 6] class = 1 2971->2983 2973 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2972->2973 2974 age <= 56.0 entropy = 0.811 samples = 16 value = [4, 12] class = 1 2972->2974 2975 entropy = 0.0 samples = 3 value = [0, 3] class = 1 2974->2975 2976 age <= 60.5 entropy = 0.89 samples = 13 value = [4, 9] class = 1 2974->2976 2977 age <= 59.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 2976->2977 2980 education <= 14.5 entropy = 0.544 samples = 8 value = [1, 7] class = 1 2976->2980 2978 entropy = 1.0 samples = 4 value = [2, 2] class = 0 2977->2978 2979 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2977->2979 2981 entropy = 0.0 samples = 6 value = [0, 6] class = 1 2980->2981 2982 entropy = 1.0 samples = 2 value = [1, 1] class = 0 2980->2982 2988 age <= 55.0 entropy = 1.0 samples = 28 value = [14, 14] class = 0 2987->2988 3011 entropy = 0.0 samples = 4 value = [4, 0] class = 0 2987->3011 2989 education <= 13.5 entropy = 0.949 samples = 19 value = [7, 12] class = 1 2988->2989 3006 workclass_Self-emp <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] class = 0 2988->3006 2990 age <= 39.0 entropy = 1.0 samples = 12 value = [6, 6] class = 0 2989->2990 3003 age <= 38.5 entropy = 0.592 samples = 7 value = [1, 6] class = 1 2989->3003 2991 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2990->2991 2992 age <= 44.5 entropy = 0.994 samples = 11 value = [5, 6] class = 1 2990->2992 2993 entropy = 0.0 samples = 2 value = [0, 2] class = 1 2992->2993 2994 age <= 46.5 entropy = 0.991 samples = 9 value = [5, 4] class = 0 2992->2994 2995 entropy = 0.0 samples = 2 value = [2, 0] class = 0 2994->2995 2996 age <= 52.0 entropy = 0.985 samples = 7 value = [3, 4] class = 1 2994->2996 2997 age <= 50.0 entropy = 0.918 samples = 6 value = [2, 4] class = 1 2996->2997 3002 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2996->3002 2998 workclass_Self-emp <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 2997->2998 3001 entropy = 0.0 samples = 1 value = [0, 1] class = 1 2997->3001 2999 entropy = 0.811 samples = 4 value = [1, 3] class = 1 2998->2999 3000 entropy = 0.0 samples = 1 value = [1, 0] class = 0 2998->3000 3004 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3003->3004 3005 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3003->3005 3007 education <= 14.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 3006->3007 3010 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3006->3010 3008 entropy = 0.0 samples = 5 value = [5, 0] class = 0 3007->3008 3009 entropy = 0.918 samples = 3 value = [2, 1] class = 0 3007->3009 3013 hours-per-week <= 48.5 entropy = 0.958 samples = 137 value = [52, 85] class = 1 3012->3013 3116 education <= 14.5 entropy = 0.716 samples = 584 value = [115, 469] class = 1 3012->3116 3014 sex_Male <= 0.5 entropy = 0.828 samples = 46 value = [12, 34] class = 1 3013->3014 3043 age <= 24.5 entropy = 0.989 samples = 91 value = [40, 51] class = 1 3013->3043 3015 entropy = 0.0 samples = 6 value = [0, 6] class = 1 3014->3015 3016 education <= 13.5 entropy = 0.881 samples = 40 value = [12, 28] class = 1 3014->3016 3017 age <= 27.5 entropy = 0.928 samples = 35 value = [12, 23] class = 1 3016->3017 3042 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3016->3042 3018 age <= 24.5 entropy = 0.544 samples = 8 value = [1, 7] class = 1 3017->3018 3021 workclass_Self-emp <= 0.5 entropy = 0.975 samples = 27 value = [11, 16] class = 1 3017->3021 3019 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3018->3019 3020 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3018->3020 3022 education <= 12.5 entropy = 0.99 samples = 25 value = [11, 14] class = 1 3021->3022 3041 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3021->3041 3023 age <= 31.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 3022->3023 3026 age <= 31.0 entropy = 0.949 samples = 19 value = [7, 12] class = 1 3022->3026 3024 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3023->3024 3025 entropy = 0.0 samples = 3 value = [3, 0] class = 0 3023->3025 3027 race_White <= 0.5 entropy = 0.994 samples = 11 value = [6, 5] class = 0 3026->3027 3036 workclass_Private <= 0.5 entropy = 0.544 samples = 8 value = [1, 7] class = 1 3026->3036 3028 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3027->3028 3029 hours-per-week <= 44.0 entropy = 0.971 samples = 10 value = [6, 4] class = 0 3027->3029 3030 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3029->3030 3031 age <= 29.5 entropy = 0.991 samples = 9 value = [5, 4] class = 0 3029->3031 3032 age <= 28.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 3031->3032 3035 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3031->3035 3033 entropy = 0.971 samples = 5 value = [3, 2] class = 0 3032->3033 3034 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3032->3034 3037 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3036->3037 3038 age <= 32.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 3036->3038 3039 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3038->3039 3040 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3038->3040 3044 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3043->3044 3045 race_Black <= 0.5 entropy = 0.985 samples = 89 value = [38, 51] class = 1 3043->3045 3046 age <= 29.5 entropy = 0.988 samples = 87 value = [38, 49] class = 1 3045->3046 3115 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3045->3115 3047 race_Asian <= 0.5 entropy = 0.993 samples = 31 value = [17, 14] class = 0 3046->3047 3078 hours-per-week <= 72.5 entropy = 0.954 samples = 56 value = [21, 35] class = 1 3046->3078 3048 education <= 15.5 entropy = 0.999 samples = 29 value = [15, 14] class = 0 3047->3048 3077 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3047->3077 3049 age <= 28.5 entropy = 0.996 samples = 28 value = [15, 13] class = 0 3048->3049 3076 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3048->3076 3050 hours-per-week <= 65.0 entropy = 0.991 samples = 18 value = [8, 10] class = 1 3049->3050 3065 education <= 13.5 entropy = 0.881 samples = 10 value = [7, 3] class = 0 3049->3065 3051 education <= 12.5 entropy = 1.0 samples = 16 value = [8, 8] class = 0 3050->3051 3064 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3050->3064 3052 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3051->3052 3053 hours-per-week <= 57.5 entropy = 0.997 samples = 15 value = [7, 8] class = 1 3051->3053 3054 sex_Female <= 0.5 entropy = 0.994 samples = 11 value = [6, 5] class = 0 3053->3054 3063 entropy = 0.811 samples = 4 value = [1, 3] class = 1 3053->3063 3055 workclass_Private <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] class = 0 3054->3055 3062 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3054->3062 3056 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3055->3056 3057 hours-per-week <= 52.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 3055->3057 3058 age <= 26.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 3057->3058 3061 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3057->3061 3059 entropy = 0.918 samples = 3 value = [2, 1] class = 0 3058->3059 3060 entropy = 0.0 samples = 3 value = [3, 0] class = 0 3058->3060 3066 hours-per-week <= 65.0 entropy = 0.954 samples = 8 value = [5, 3] class = 0 3065->3066 3075 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3065->3075 3067 workclass_Public <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 3066->3067 3074 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3066->3074 3068 education <= 12.5 entropy = 0.65 samples = 6 value = [5, 1] class = 0 3067->3068 3073 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3067->3073 3069 hours-per-week <= 55.0 entropy = 0.722 samples = 5 value = [4, 1] class = 0 3068->3069 3072 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3068->3072 3070 entropy = 0.811 samples = 4 value = [3, 1] class = 0 3069->3070 3071 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3069->3071 3079 hours-per-week <= 62.5 entropy = 0.964 samples = 54 value = [21, 33] class = 1 3078->3079 3114 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3078->3114 3080 workclass_Public <= 0.5 entropy = 0.931 samples = 49 value = [17, 32] class = 1 3079->3080 3111 age <= 30.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 3079->3111 3081 age <= 31.5 entropy = 0.893 samples = 42 value = [13, 29] class = 1 3080->3081 3106 age <= 31.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 3080->3106 3082 education <= 13.5 entropy = 0.991 samples = 18 value = [8, 10] class = 1 3081->3082 3093 hours-per-week <= 57.5 entropy = 0.738 samples = 24 value = [5, 19] class = 1 3081->3093 3083 hours-per-week <= 53.5 entropy = 0.918 samples = 15 value = [5, 10] class = 1 3082->3083 3092 entropy = 0.0 samples = 3 value = [3, 0] class = 0 3082->3092 3084 age <= 30.5 entropy = 0.503 samples = 9 value = [1, 8] class = 1 3083->3084 3089 education <= 12.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 3083->3089 3085 education <= 12.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 3084->3085 3088 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3084->3088 3086 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3085->3086 3087 entropy = 0.811 samples = 4 value = [1, 3] class = 1 3085->3087 3090 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3089->3090 3091 entropy = 1.0 samples = 4 value = [2, 2] class = 0 3089->3091 3094 workclass_Private <= 0.5 entropy = 0.896 samples = 16 value = [5, 11] class = 1 3093->3094 3105 entropy = 0.0 samples = 8 value = [0, 8] class = 1 3093->3105 3095 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3094->3095 3096 education <= 14.5 entropy = 0.961 samples = 13 value = [5, 8] class = 1 3094->3096 3097 age <= 32.5 entropy = 0.994 samples = 11 value = [5, 6] class = 1 3096->3097 3104 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3096->3104 3098 entropy = 0.811 samples = 4 value = [1, 3] class = 1 3097->3098 3099 hours-per-week <= 52.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 3097->3099 3100 education <= 13.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 3099->3100 3103 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3099->3103 3101 entropy = 0.918 samples = 3 value = [2, 1] class = 0 3100->3101 3102 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3100->3102 3107 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3106->3107 3108 age <= 32.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 3106->3108 3109 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3108->3109 3110 entropy = 0.918 samples = 3 value = [2, 1] class = 0 3108->3110 3112 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3111->3112 3113 entropy = 0.918 samples = 3 value = [2, 1] class = 0 3111->3113 3117 race_White <= 0.5 entropy = 0.776 samples = 472 value = [108, 364] class = 1 3116->3117 3376 age <= 56.5 entropy = 0.337 samples = 112 value = [7, 105] class = 1 3116->3376 3118 workclass_Public <= 0.5 entropy = 0.99 samples = 25 value = [14, 11] class = 0 3117->3118 3133 hours-per-week <= 85.0 entropy = 0.742 samples = 447 value = [94, 353] class = 1 3117->3133 3119 workclass_Self-emp <= 0.5 entropy = 0.946 samples = 22 value = [14, 8] class = 0 3118->3119 3132 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3118->3132 3120 age <= 42.5 entropy = 1.0 samples = 14 value = [7, 7] class = 0 3119->3120 3129 race_Asian <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] class = 0 3119->3129 3121 hours-per-week <= 52.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 3120->3121 3126 race_Black <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] class = 0 3120->3126 3122 age <= 37.0 entropy = 0.971 samples = 5 value = [2, 3] class = 1 3121->3122 3125 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3121->3125 3123 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3122->3123 3124 entropy = 0.811 samples = 4 value = [1, 3] class = 1 3122->3124 3127 entropy = 0.0 samples = 4 value = [4, 0] class = 0 3126->3127 3128 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3126->3128 3130 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3129->3130 3131 entropy = 0.0 samples = 6 value = [6, 0] class = 0 3129->3131 3134 age <= 41.5 entropy = 0.729 samples = 442 value = [90, 352] class = 1 3133->3134 3373 age <= 40.5 entropy = 0.722 samples = 5 value = [4, 1] class = 0 3133->3373 3135 age <= 40.5 entropy = 0.6 samples = 178 value = [26, 152] class = 1 3134->3135 3208 workclass_Private <= 0.5 entropy = 0.799 samples = 264 value = [64, 200] class = 1 3134->3208 3136 education <= 12.5 entropy = 0.635 samples = 162 value = [26, 136] class = 1 3135->3136 3207 entropy = 0.0 samples = 16 value = [0, 16] class = 1 3135->3207 3137 entropy = 0.0 samples = 12 value = [0, 12] class = 1 3136->3137 3138 hours-per-week <= 57.5 entropy = 0.665 samples = 150 value = [26, 124] class = 1 3136->3138 3139 workclass_Public <= 0.5 entropy = 0.592 samples = 112 value = [16, 96] class = 1 3138->3139 3188 education <= 13.5 entropy = 0.831 samples = 38 value = [10, 28] class = 1 3138->3188 3140 hours-per-week <= 49.0 entropy = 0.642 samples = 98 value = [16, 82] class = 1 3139->3140 3187 entropy = 0.0 samples = 14 value = [0, 14] class = 1 3139->3187 3141 hours-per-week <= 44.0 entropy = 0.811 samples = 36 value = [9, 27] class = 1 3140->3141 3164 age <= 38.225 entropy = 0.509 samples = 62 value = [7, 55] class = 1 3140->3164 3142 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3141->3142 3143 age <= 35.5 entropy = 0.845 samples = 33 value = [9, 24] class = 1 3141->3143 3144 hours-per-week <= 47.5 entropy = 0.684 samples = 11 value = [2, 9] class = 1 3143->3144 3149 hours-per-week <= 45.5 entropy = 0.902 samples = 22 value = [7, 15] class = 1 3143->3149 3145 age <= 34.5 entropy = 0.469 samples = 10 value = [1, 9] class = 1 3144->3145 3148 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3144->3148 3146 entropy = 0.811 samples = 4 value = [1, 3] class = 1 3145->3146 3147 entropy = 0.0 samples = 6 value = [0, 6] class = 1 3145->3147 3150 sex_Female <= 0.5 entropy = 0.949 samples = 19 value = [7, 12] class = 1 3149->3150 3163 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3149->3163 3151 workclass_Self-emp <= 0.5 entropy = 0.964 samples = 18 value = [7, 11] class = 1 3150->3151 3162 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3150->3162 3152 age <= 39.5 entropy = 0.918 samples = 15 value = [5, 10] class = 1 3151->3152 3161 entropy = 0.918 samples = 3 value = [2, 1] class = 0 3151->3161 3153 age <= 38.5 entropy = 0.863 samples = 14 value = [4, 10] class = 1 3152->3153 3160 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3152->3160 3154 education <= 13.5 entropy = 0.918 samples = 12 value = [4, 8] class = 1 3153->3154 3159 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3153->3159 3155 age <= 36.5 entropy = 0.811 samples = 8 value = [2, 6] class = 1 3154->3155 3158 entropy = 1.0 samples = 4 value = [2, 2] class = 0 3154->3158 3156 entropy = 1.0 samples = 4 value = [2, 2] class = 0 3155->3156 3157 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3155->3157 3165 age <= 34.5 entropy = 0.607 samples = 47 value = [7, 40] class = 1 3164->3165 3186 entropy = 0.0 samples = 15 value = [0, 15] class = 1 3164->3186 3166 entropy = 0.0 samples = 10 value = [0, 10] class = 1 3165->3166 3167 education <= 13.5 entropy = 0.7 samples = 37 value = [7, 30] class = 1 3165->3167 3168 workclass_Self-emp <= 0.5 entropy = 0.746 samples = 33 value = [7, 26] class = 1 3167->3168 3185 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3167->3185 3169 age <= 37.5 entropy = 0.771 samples = 31 value = [7, 24] class = 1 3168->3169 3184 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3168->3184 3170 hours-per-week <= 53.5 entropy = 0.845 samples = 22 value = [6, 16] class = 1 3169->3170 3181 hours-per-week <= 52.5 entropy = 0.503 samples = 9 value = [1, 8] class = 1 3169->3181 3171 hours-per-week <= 51.0 entropy = 0.764 samples = 18 value = [4, 14] class = 1 3170->3171 3180 entropy = 1.0 samples = 4 value = [2, 2] class = 0 3170->3180 3172 sex_Female <= 0.5 entropy = 0.811 samples = 16 value = [4, 12] class = 1 3171->3172 3179 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3171->3179 3173 age <= 35.5 entropy = 0.837 samples = 15 value = [4, 11] class = 1 3172->3173 3178 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3172->3178 3174 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3173->3174 3175 age <= 36.5 entropy = 0.811 samples = 12 value = [3, 9] class = 1 3173->3175 3176 entropy = 0.722 samples = 5 value = [1, 4] class = 1 3175->3176 3177 entropy = 0.863 samples = 7 value = [2, 5] class = 1 3175->3177 3182 entropy = 0.592 samples = 7 value = [1, 6] class = 1 3181->3182 3183 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3181->3183 3189 sex_Female <= 0.5 entropy = 0.94 samples = 28 value = [10, 18] class = 1 3188->3189 3206 entropy = 0.0 samples = 10 value = [0, 10] class = 1 3188->3206 3190 hours-per-week <= 75.0 entropy = 0.918 samples = 27 value = [9, 18] class = 1 3189->3190 3205 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3189->3205 3191 hours-per-week <= 62.5 entropy = 0.943 samples = 25 value = [9, 16] class = 1 3190->3191 3204 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3190->3204 3192 age <= 39.5 entropy = 0.863 samples = 21 value = [6, 15] class = 1 3191->3192 3203 entropy = 0.811 samples = 4 value = [3, 1] class = 0 3191->3203 3193 workclass_Public <= 0.5 entropy = 0.764 samples = 18 value = [4, 14] class = 1 3192->3193 3202 entropy = 0.918 samples = 3 value = [2, 1] class = 0 3192->3202 3194 age <= 34.5 entropy = 0.696 samples = 16 value = [3, 13] class = 1 3193->3194 3201 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3193->3201 3195 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3194->3195 3196 age <= 35.5 entropy = 0.845 samples = 11 value = [3, 8] class = 1 3194->3196 3197 entropy = 1.0 samples = 4 value = [2, 2] class = 0 3196->3197 3198 age <= 38.725 entropy = 0.592 samples = 7 value = [1, 6] class = 1 3196->3198 3199 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3198->3199 3200 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3198->3200 3209 education <= 12.5 entropy = 0.879 samples = 114 value = [34, 80] class = 1 3208->3209 3290 age <= 80.0 entropy = 0.722 samples = 150 value = [30, 120] class = 1 3208->3290 3210 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3209->3210 3211 education <= 13.5 entropy = 0.863 samples = 112 value = [32, 80] class = 1 3209->3211 3212 hours-per-week <= 53.5 entropy = 0.909 samples = 71 value = [23, 48] class = 1 3211->3212 3271 age <= 43.5 entropy = 0.759 samples = 41 value = [9, 32] class = 1 3211->3271 3213 age <= 82.5 entropy = 0.811 samples = 44 value = [11, 33] class = 1 3212->3213 3248 workclass_Public <= 0.5 entropy = 0.991 samples = 27 value = [12, 15] class = 1 3212->3248 3214 age <= 65.5 entropy = 0.782 samples = 43 value = [10, 33] class = 1 3213->3214 3247 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3213->3247 3215 age <= 61.5 entropy = 0.801 samples = 41 value = [10, 31] class = 1 3214->3215 3246 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3214->3246 3216 sex_Male <= 0.5 entropy = 0.769 samples = 40 value = [9, 31] class = 1 3215->3216 3245 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3215->3245 3217 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3216->3217 3218 age <= 60.5 entropy = 0.822 samples = 35 value = [9, 26] class = 1 3216->3218 3219 age <= 58.0 entropy = 0.845 samples = 33 value = [9, 24] class = 1 3218->3219 3244 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3218->3244 3220 age <= 51.5 entropy = 0.811 samples = 32 value = [8, 24] class = 1 3219->3220 3243 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3219->3243 3221 hours-per-week <= 46.5 entropy = 0.877 samples = 27 value = [8, 19] class = 1 3220->3221 3242 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3220->3242 3222 age <= 43.5 entropy = 0.971 samples = 10 value = [4, 6] class = 1 3221->3222 3231 age <= 42.5 entropy = 0.787 samples = 17 value = [4, 13] class = 1 3221->3231 3223 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3222->3223 3224 age <= 50.5 entropy = 0.918 samples = 9 value = [3, 6] class = 1 3222->3224 3225 age <= 47.5 entropy = 0.811 samples = 8 value = [2, 6] class = 1 3224->3225 3230 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3224->3230 3226 workclass_Public <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 3225->3226 3229 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3225->3229 3227 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3226->3227 3228 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3226->3228 3232 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3231->3232 3233 age <= 43.5 entropy = 0.89 samples = 13 value = [4, 9] class = 1 3231->3233 3234 entropy = 0.918 samples = 3 value = [2, 1] class = 0 3233->3234 3235 age <= 45.5 entropy = 0.722 samples = 10 value = [2, 8] class = 1 3233->3235 3236 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3235->3236 3237 age <= 46.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 3235->3237 3238 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3237->3238 3239 age <= 48.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 3237->3239 3240 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3239->3240 3241 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3239->3241 3249 age <= 59.0 entropy = 0.954 samples = 24 value = [9, 15] class = 1 3248->3249 3270 entropy = 0.0 samples = 3 value = [3, 0] class = 0 3248->3270 3250 age <= 46.5 entropy = 0.993 samples = 20 value = [9, 11] class = 1 3249->3250 3269 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3249->3269 3251 age <= 42.5 entropy = 0.811 samples = 8 value = [2, 6] class = 1 3250->3251 3256 age <= 55.5 entropy = 0.98 samples = 12 value = [7, 5] class = 0 3250->3256 3252 hours-per-week <= 75.0 entropy = 0.971 samples = 5 value = [2, 3] class = 1 3251->3252 3255 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3251->3255 3253 entropy = 1.0 samples = 4 value = [2, 2] class = 0 3252->3253 3254 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3252->3254 3257 age <= 53.5 entropy = 0.994 samples = 11 value = [6, 5] class = 0 3256->3257 3268 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3256->3268 3258 hours-per-week <= 57.5 entropy = 0.971 samples = 10 value = [6, 4] class = 0 3257->3258 3267 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3257->3267 3259 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3258->3259 3260 hours-per-week <= 67.5 entropy = 0.991 samples = 9 value = [5, 4] class = 0 3258->3260 3261 hours-per-week <= 62.5 entropy = 1.0 samples = 8 value = [4, 4] class = 0 3260->3261 3266 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3260->3266 3262 age <= 48.5 entropy = 0.985 samples = 7 value = [4, 3] class = 0 3261->3262 3265 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3261->3265 3263 entropy = 0.918 samples = 3 value = [2, 1] class = 0 3262->3263 3264 entropy = 1.0 samples = 4 value = [2, 2] class = 0 3262->3264 3272 workclass_Public <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] class = 0 3271->3272 3275 age <= 49.5 entropy = 0.65 samples = 36 value = [6, 30] class = 1 3271->3275 3273 entropy = 0.0 samples = 3 value = [3, 0] class = 0 3272->3273 3274 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3272->3274 3276 entropy = 0.0 samples = 14 value = [0, 14] class = 1 3275->3276 3277 sex_Female <= 0.5 entropy = 0.845 samples = 22 value = [6, 16] class = 1 3275->3277 3278 age <= 50.5 entropy = 0.722 samples = 20 value = [4, 16] class = 1 3277->3278 3289 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3277->3289 3279 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3278->3279 3280 age <= 57.5 entropy = 0.629 samples = 19 value = [3, 16] class = 1 3278->3280 3281 workclass_Public <= 0.5 entropy = 0.779 samples = 13 value = [3, 10] class = 1 3280->3281 3288 entropy = 0.0 samples = 6 value = [0, 6] class = 1 3280->3288 3282 age <= 54.5 entropy = 0.592 samples = 7 value = [1, 6] class = 1 3281->3282 3285 age <= 53.0 entropy = 0.918 samples = 6 value = [2, 4] class = 1 3281->3285 3283 entropy = 0.811 samples = 4 value = [1, 3] class = 1 3282->3283 3284 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3282->3284 3286 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3285->3286 3287 entropy = 0.811 samples = 4 value = [1, 3] class = 1 3285->3287 3291 age <= 50.5 entropy = 0.711 samples = 149 value = [29, 120] class = 1 3290->3291 3372 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3290->3372 3292 hours-per-week <= 67.5 entropy = 0.764 samples = 99 value = [22, 77] class = 1 3291->3292 3347 hours-per-week <= 49.0 entropy = 0.584 samples = 50 value = [7, 43] class = 1 3291->3347 3293 education <= 12.5 entropy = 0.75 samples = 98 value = [21, 77] class = 1 3292->3293 3346 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3292->3346 3294 age <= 46.5 entropy = 0.971 samples = 10 value = [4, 6] class = 1 3293->3294 3299 age <= 44.5 entropy = 0.708 samples = 88 value = [17, 71] class = 1 3293->3299 3295 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3294->3295 3296 hours-per-week <= 47.5 entropy = 0.918 samples = 6 value = [4, 2] class = 0 3294->3296 3297 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3296->3297 3298 entropy = 1.0 samples = 4 value = [2, 2] class = 0 3296->3298 3300 education <= 13.5 entropy = 0.822 samples = 35 value = [9, 26] class = 1 3299->3300 3323 hours-per-week <= 44.5 entropy = 0.612 samples = 53 value = [8, 45] class = 1 3299->3323 3301 hours-per-week <= 62.5 entropy = 0.89 samples = 26 value = [8, 18] class = 1 3300->3301 3318 hours-per-week <= 47.5 entropy = 0.503 samples = 9 value = [1, 8] class = 1 3300->3318 3302 hours-per-week <= 56.5 entropy = 0.904 samples = 25 value = [8, 17] class = 1 3301->3302 3317 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3301->3317 3303 age <= 42.5 entropy = 0.874 samples = 17 value = [5, 12] class = 1 3302->3303 3310 hours-per-week <= 59.0 entropy = 0.954 samples = 8 value = [3, 5] class = 1 3302->3310 3304 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3303->3304 3305 hours-per-week <= 47.5 entropy = 0.961 samples = 13 value = [5, 8] class = 1 3303->3305 3306 entropy = 0.918 samples = 3 value = [2, 1] class = 0 3305->3306 3307 age <= 43.5 entropy = 0.881 samples = 10 value = [3, 7] class = 1 3305->3307 3308 entropy = 0.971 samples = 5 value = [2, 3] class = 1 3307->3308 3309 entropy = 0.722 samples = 5 value = [1, 4] class = 1 3307->3309 3311 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3310->3311 3312 age <= 42.5 entropy = 0.863 samples = 7 value = [2, 5] class = 1 3310->3312 3313 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3312->3313 3314 age <= 43.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 3312->3314 3315 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3314->3315 3316 entropy = 0.811 samples = 4 value = [1, 3] class = 1 3314->3316 3319 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3318->3319 3320 age <= 42.5 entropy = 0.722 samples = 5 value = [1, 4] class = 1 3318->3320 3321 entropy = 0.0 samples = 2 value = [0, 2] class = 1 3320->3321 3322 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3320->3322 3324 entropy = 1.0 samples = 4 value = [2, 2] class = 0 3323->3324 3325 hours-per-week <= 53.5 entropy = 0.536 samples = 49 value = [6, 43] class = 1 3323->3325 3326 age <= 47.5 entropy = 0.345 samples = 31 value = [2, 29] class = 1 3325->3326 3335 age <= 49.5 entropy = 0.764 samples = 18 value = [4, 14] class = 1 3325->3335 3327 entropy = 0.0 samples = 13 value = [0, 13] class = 1 3326->3327 3328 education <= 13.5 entropy = 0.503 samples = 18 value = [2, 16] class = 1 3326->3328 3329 hours-per-week <= 47.5 entropy = 0.619 samples = 13 value = [2, 11] class = 1 3328->3329 3334 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3328->3334 3330 age <= 48.5 entropy = 0.503 samples = 9 value = [1, 8] class = 1 3329->3330 3333 entropy = 0.811 samples = 4 value = [1, 3] class = 1 3329->3333 3331 entropy = 0.0 samples = 6 value = [0, 6] class = 1 3330->3331 3332 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3330->3332 3336 age <= 46.5 entropy = 0.696 samples = 16 value = [3, 13] class = 1 3335->3336 3345 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3335->3345 3337 education <= 13.5 entropy = 0.845 samples = 11 value = [3, 8] class = 1 3336->3337 3344 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3336->3344 3338 hours-per-week <= 57.5 entropy = 0.811 samples = 8 value = [2, 6] class = 1 3337->3338 3343 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3337->3343 3339 age <= 45.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 3338->3339 3342 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3338->3342 3340 entropy = 1.0 samples = 4 value = [2, 2] class = 0 3339->3340 3341 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3339->3341 3348 entropy = 0.0 samples = 11 value = [0, 11] class = 1 3347->3348 3349 hours-per-week <= 62.5 entropy = 0.679 samples = 39 value = [7, 32] class = 1 3347->3349 3350 age <= 55.5 entropy = 0.722 samples = 35 value = [7, 28] class = 1 3349->3350 3371 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3349->3371 3351 age <= 54.5 entropy = 0.559 samples = 23 value = [3, 20] class = 1 3350->3351 3364 education <= 13.5 entropy = 0.918 samples = 12 value = [4, 8] class = 1 3350->3364 3352 age <= 53.5 entropy = 0.65 samples = 18 value = [3, 15] class = 1 3351->3352 3363 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3351->3363 3353 hours-per-week <= 52.5 entropy = 0.523 samples = 17 value = [2, 15] class = 1 3352->3353 3362 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3352->3362 3354 education <= 13.5 entropy = 0.764 samples = 9 value = [2, 7] class = 1 3353->3354 3361 entropy = 0.0 samples = 8 value = [0, 8] class = 1 3353->3361 3355 age <= 52.5 entropy = 0.918 samples = 6 value = [2, 4] class = 1 3354->3355 3360 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3354->3360 3356 age <= 51.5 entropy = 0.971 samples = 5 value = [2, 3] class = 1 3355->3356 3359 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3355->3359 3357 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3356->3357 3358 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3356->3358 3365 age <= 57.5 entropy = 0.65 samples = 6 value = [1, 5] class = 1 3364->3365 3368 hours-per-week <= 57.5 entropy = 1.0 samples = 6 value = [3, 3] class = 0 3364->3368 3366 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3365->3366 3367 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3365->3367 3369 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3368->3369 3370 entropy = 0.811 samples = 4 value = [1, 3] class = 1 3368->3370 3374 entropy = 0.0 samples = 1 value = [0, 1] class = 1 3373->3374 3375 entropy = 0.0 samples = 4 value = [4, 0] class = 0 3373->3375 3377 age <= 38.725 entropy = 0.248 samples = 97 value = [4, 93] class = 1 3376->3377 3396 age <= 66.5 entropy = 0.722 samples = 15 value = [3, 12] class = 1 3376->3396 3378 age <= 35.5 entropy = 0.61 samples = 20 value = [3, 17] class = 1 3377->3378 3389 education <= 15.5 entropy = 0.1 samples = 77 value = [1, 76] class = 1 3377->3389 3379 entropy = 0.0 samples = 7 value = [0, 7] class = 1 3378->3379 3380 age <= 36.5 entropy = 0.779 samples = 13 value = [3, 10] class = 1 3378->3380 3381 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3380->3381 3382 hours-per-week <= 65.0 entropy = 0.65 samples = 12 value = [2, 10] class = 1 3380->3382 3383 hours-per-week <= 55.0 entropy = 0.811 samples = 8 value = [2, 6] class = 1 3382->3383 3388 entropy = 0.0 samples = 4 value = [0, 4] class = 1 3382->3388 3384 age <= 38.225 entropy = 0.65 samples = 6 value = [1, 5] class = 1 3383->3384 3387 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3383->3387 3385 entropy = 0.0 samples = 3 value = [0, 3] class = 1 3384->3385 3386 entropy = 0.918 samples = 3 value = [1, 2] class = 1 3384->3386 3390 entropy = 0.0 samples = 48 value = [0, 48] class = 1 3389->3390 3391 workclass_Self-emp <= 0.5 entropy = 0.216 samples = 29 value = [1, 28] class = 1 3389->3391 3392 entropy = 0.0 samples = 22 value = [0, 22] class = 1 3391->3392 3393 hours-per-week <= 55.0 entropy = 0.592 samples = 7 value = [1, 6] class = 1 3391->3393 3394 entropy = 1.0 samples = 2 value = [1, 1] class = 0 3393->3394 3395 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3393->3395 3397 workclass_Private <= 0.5 entropy = 0.881 samples = 10 value = [3, 7] class = 1 3396->3397 3402 entropy = 0.0 samples = 5 value = [0, 5] class = 1 3396->3402 3398 age <= 65.0 entropy = 0.544 samples = 8 value = [1, 7] class = 1 3397->3398 3401 entropy = 0.0 samples = 2 value = [2, 0] class = 0 3397->3401 3399 entropy = 0.0 samples = 7 value = [0, 7] class = 1 3398->3399 3400 entropy = 0.0 samples = 1 value = [1, 0] class = 0 3398->3400
In [36]:
system(dot -Tpng tree.dot -o dtree.png)
Out[36]:
[]
In [37]:
from IPython.display import Image
Image(filename='dtree.png')
Out[37]:
In [38]:
print("Feature Importances:\n{}".format(treeclf.feature_importances_))
Feature Importances:
[0.28 0.21 0.15 0.02 0.02 0.02 0.23 0.   0.   0.01 0.01 0.   0.02 0.01 0.01]
In [39]:
import pylab as plt
%matplotlib inline

def plot_feature_importances(model, n_features, feature_names):
    plt.barh(range(n_features), model.feature_importances_, align='center')
    plt.yticks(np.arange(n_features), feature_names)
    plt.xlabel("Feature importance")
    plt.ylabel("Feature")
    plt.ylim(-1, n_features)

plot_feature_importances(treeclf, len(data_all.columns), data_all.columns)